AigeoRadar

AigeoRadar · AI Context Benchmark

AI Context Benchmark

Question → sufficient layer(s) → retrieved slice → measured cost → evidence

WordPress GEO: llms.txt and AI Discovery Guide | AigeoRadar — backlog sprint

https://aigeoradar.com/blog/wordpress-geo-llms-txt-guide

20 questions Answer LLM: gpt-4o-mini ai_context_benchmark_v1

Of 20 questions: lowest measured retrieval cost among sufficient layers — HTML 6 · Schema 7 · AIPM 6 · multi-layer 0 · unanswered 1. Descriptive counts only; no overall ranking.

Of 20 questions: lowest measured retrieval cost among sufficient layers — HTML 6 · Schema 7 · AIPM 6 · multi-layer 0 · unanswered 1. Descriptive counts only; no overall ranking. Each question shows: sufficient layer(s) → retrieved slice → measured input tokens → evidence. Readers interpret; the report does not rank formats. Semantic Redundancy 25% — moderate field overlap; trim duplicated purpose/summary/keyFacts wording.

Gold mode · Independent HTML gold

Gold answers are derived from the HTML page (title, h1, lang, prose) — not from AIPM fields. Understanding/Retrieval therefore measure layer capability, not AIPM self-consistency. Human-authored gold.json remains the gold standard for publication-grade claims.

Execution provenance

Methodology
AI Context Benchmark Methodology v1.0
Planner Spec
v1.0
Execution Protocol
v1.0
Question pack
universal_v1
Score version
aipm_benchmark_score_v10
Engine
aipm_benchmark_v4
Answer LLM
openai / gpt-4o-mini (single provider this lab)
Model snapshot
2026-07
Run id
1814596c-39a3-490f-98d8-de04f6348876

Question outcomes

Per question: which layers were sufficient, and what was the lowest measured retrieval cost among them. No overall winner.

6

Lowest cost: HTML

7

Lowest cost: Schema

6

Lowest cost: AIPM

0

Multi-layer

1

Unanswered

Manifest design

Semantic Redundancy · 25%

Semantic Redundancy 25% — moderate field overlap; trim duplicated purpose/summary/keyFacts wording.

  • →Reduce repeated wording across purpose, abstract, keyFacts, and sections.
Field A Field B Overlap
purpose primaryTopic 25%

Structural size (secondary)

Structural size is secondary. A larger AIPM is not a failure if per-question retrieval stays tiny — check Semantic Redundancy instead.

HTML · 22,175 chars
Schema · 15,629 chars
AIPM · 35,339 chars

Routing helper (secondary)

Illustrative card-first vs HTML-always simulation — prefer per-question measured cost above. Not a ranking.

Card-first retrieval would cost ~14% more tokens than HTML-always on this pack — machine card is heavier here.

Orientation pack

10 questions · all layers scored

  • HTML 8/10
  • Schema 4/10
  • AIPM 9/10

Depth pack

10 questions · all layers scored

  • HTML 9/10
  • Schema 5/10
  • AIPM 7/10

Full-context pack metrics (secondary)

These measure the whole file fed to the model this run — not the minimum slice needed per question.

HTML

17/20 matched

9,604 full-pack tokens

Schema

9/20 matched

9,125 full-pack tokens

AIPM

16/20 matched

13,600 full-pack tokens

Full-pack resource table (secondary)

Whole-file context fed this run. Prefer Minimal Retrieval Cost on each question.

Metric HTML Schema AIPM
Coverage (matched) 17/20 9/20 16/20
Context size 22,175 chars 15,629 chars 35,339 chars
Total tokens 9,604 9,125 13,600
Tokens / correct answer 565 1,014 850
Est. cost / correct answer $0.000095 $0.000166 $0.000139
Matched per 1k tokens 1.770 0.986 1.176
Median latency 1,289 ms 1,386 ms 1,286 ms
Est. cost (USD) $0.00161 $0.00149 $0.00223

Six independent scores

Answer Efficiency is the primary cost lens. Accuracy axes remain for research — no combined total or winner.

Answer Efficiency

Matched answers per 1k tokens (and cost per match). The primary efficiency axis — not raw accuracy.

  • HTML 100
  • Schema 55.7
  • AIPM 66.4

Understanding

Can this layer convey what the page is about — using independent HTML gold?

  • HTML 85.7
  • Schema 28.6
  • AIPM 85.7

Retrieval

Can this layer surface shared facts (location, contact, hours, pricing, FAQ, CTA)?

  • HTML 88.9
  • Schema 44.4
  • AIPM 66.7

Evidence

Answer quality vs independent gold (score strength). Partial credit counts; UNKNOWN scores zero unless gold is UNKNOWN.

  • HTML 73.2
  • Schema 47.8
  • AIPM 73.6

Metadata

Language, page kind, and freshness from page signals.

  • HTML 66.7
  • Schema 66.7
  • AIPM 100

Compression

Information delivered per token and context size. Higher means more matched answers for less context cost.

  • HTML 100
  • Schema 55.7
  • AIPM 66.4

Coverage map

AIPM matched 16 question(s) (alone on Q5, Q20); HTML matched 17. HTML/Schema (or a gap) still needed on Q7, Q13, Q16, Q17. No layer matched gold on Q13. Read this as complementary coverage — AIPM orients agents cheaply; HTML supplies depth when the sidecar cannot. HTML 17/20 matched (≈565 tok/match). Schema 9/20 matched (≈1,014 tok/match). AIPM 16/20 matched (≈850 tok/match). Figures are descriptive per layer — not a ranking. See per-question chains for sufficient layers, slice size, and measured cost.

AIPM matched

Q1, Q2, Q3, Q4, Q5, Q6, Q8, Q9, Q10, Q11, Q12, Q14, Q15, Q18, Q19, Q20

Alone: Q5, Q20

AIPM insufficient

Q7, Q13, Q16, Q17

HTML/Schema needed or all layers missed

Unanswered by all

Q13

HTML layer

Visible page text after stripping AIPM sidecars and JSON-LD. Measures what prose alone can answer.

Context fed: 22,175 chars

Tokens: 9,604 · median 1,289 ms

Matched this pack: 17/20

Stronger on

Understanding (85.7) · Retrieval (88.9) · Evidence (73.2) · Compression (100) · Answer Efficiency (100)

Weaker on

—

Schema layer

JSON-LD structured data with minimal page chrome. Measures what schema markup can answer.

Context fed: 15,629 chars

Tokens: 9,125 · median 1,386 ms

Matched this pack: 9/20

Stronger on

—

Weaker on

Understanding (28.6)

AIPM layer

AI Page Manifest (.ai.json) only. Measures what the machine layer can answer without HTML.

Context fed: 35,339 chars

Tokens: 13,600 · median 1,286 ms

Matched this pack: 16/20

Stronger on

Understanding (85.7) · Metadata (100) · Evidence (73.6)

Weaker on

—

When to use which layer

AIPM complements HTML — it does not replace full-page prose.

Scenario Recommended Why
Fast orientation (title, purpose, brand, intent) Compare machine card → HTML fallback on this run Machine-card pack: 13,600 tok · $0.00223. Machine-card pack is heavier than HTML on this run — densify before relying on card-first retrieval.
Deep content / research (prose facts, process detail) HTML (with optional machine orientation) HTML pack: 9,604 tok · $0.00161. Use when depth needs body prose.
Structured entity pulls (org, location, typed fields) Schema.org Schema pack: 9,125 tok · $0.00149. Dense JSON-LD tends to score well here.

Findings

  • —Of 20 questions: lowest measured retrieval cost among sufficient layers — HTML 6 · Schema 7 · AIPM 6 · multi-layer 0 · unanswered 1. Descriptive counts only; no overall ranking.
  • —Semantic Redundancy 25% — moderate field overlap; trim duplicated purpose/summary/keyFacts wording.
  • —Manifest design: Reduce repeated wording across purpose, abstract, keyFacts, and sections.
  • —Structural size is secondary. A larger AIPM is not a failure if per-question retrieval stays tiny — check Semantic Redundancy instead.
  • —AIPM matched 16 question(s) (alone on Q5, Q20); HTML matched 17. HTML/Schema (or a gap) still needed on Q7, Q13, Q16, Q17. No layer matched gold on Q13. Read this as complementary coverage — AIPM orients agents cheaply; HTML supplies depth when the sidecar cannot. HTML 17/20 matched (≈565 tok/match). Schema 9/20 matched (≈1,014 tok/match). AIPM 16/20 matched (≈850 tok/match). Figures are descriptive per layer — not a ranking. See per-question chains for sufficient layers, slice size, and measured cost.

Question-by-question layer analysis

Which layer(s) could answer; which need more or different context; minimum context fed this run.

Q1 · Understanding · orientation

What is this page or business?

Gold: WordPress GEO: llms.txt, llms-full.txt, and AI discovery in 2026

Gold source: html.h1|title|jsonld.name · html_independent

Sufficient: HTML, AIPM · Partial: Schema

Measured retrieval cost (API tokens) · lowest cost SCHEMA · 517 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML title+h1+meta_description+lead_paragraphs+prose_span+body_excerpt+time 594 api 2 medium yes
Schema name+headline+description+publisher+@type+block+dateModified+datePublished 517 api 2 medium no
AIPM title+primaryTopic+purpose+abstract+publisher+keyFacts+audience+contentIntent+relatedPages+dateModified+datePublished+reviewDate+faqEntity 927 api 2 medium yes
Execution chain · AIPM · 2 round(s)
  1. Question: What is this page or business?
  2. Planner: Planner mapped question `entity_what` on AIPM to minimal fields: title, primaryTopic.
  3. Slice: title+primaryTopic+purpose+abstract+publisher+keyFacts+audience+contentIntent+relatedPages+dateModified+datePublished+reviewDate+faqEntity — AIPM.title: WordPress GEO: llms.txt and AI Discovery Guide | AigeoRadar AIPM.primaryTopic: WordPres...
  4. Actual input tokens: 927
  5. LLM answer: This page is about WordPress GEO discovery with llms.txt and aims to publish discovery assets for AI visibility using AigeoRadar.
  6. Evidence: AIPM.title: WordPress GEO: llms.txt and AI Discovery Guide | AigeoRadar AIPM.primaryTopic: WordPress GEO discovery with llms.txt AIPM.purp...
  7. Confidence: medium · matched yes

Round 1: 130 in-tok · score 0 · miss

Round 2: 797 in-tok · score 69 · match

Execution chain · HTML · 2 round(s)
  1. Question: What is this page or business?
  2. Planner: Planner mapped question `entity_what` on HTML to minimal fields: title, h1.
  3. Slice: title+h1+meta_description+lead_paragraphs+prose_span+body_excerpt+time — HTML.title: WordPress GEO: llms.txt and AI Discovery Guide | AigeoRadar — AigeoRadar HTML.h1: WordP...
  4. Actual input tokens: 594
  5. LLM answer: This page is a guide for WordPress owners on how to publish llms.txt and related AI discovery files using AigeoRadar's one-click deploy feature for Generative E...
  6. Evidence: HTML.title: WordPress GEO: llms.txt and AI Discovery Guide | AigeoRadar — AigeoRadar HTML.h1: WordPress GEO: llms.txt, llms-full.txt, and A...
  7. Confidence: medium · matched yes

Round 1: 143 in-tok · score 0 · miss

Round 2: 451 in-tok · score 58 · match

Execution chain · SCHEMA · 2 round(s)
  1. Question: What is this page or business?
  2. Planner: Planner mapped question `entity_what` on SCHEMA to minimal fields: name, headline, description.
  3. Slice: name+headline+description+publisher+@type+block+dateModified+datePublished — SCHEMA.name: AigeoRadar SCHEMA.headline: WordPress GEO: llms.txt and AI Discovery Guide | AigeoRada...
  4. Actual input tokens: 517
  5. LLM answer: This page is about AigeoRadar, an AI visibility and GEO platform that helps with website optimization and discovery assets.
  6. Evidence: SCHEMA.name: AigeoRadar SCHEMA.headline: WordPress GEO: llms.txt and AI Discovery Guide | AigeoRadar SCHEMA.description: Explains the Word...
  7. Confidence: medium · matched no

Round 1: 168 in-tok · score 50 · miss

Round 2: 349 in-tok · score 50 · miss

HTML

sufficient

This page is a guide for WordPress owners on how to publish llms.txt and related AI discovery files using AigeoRadar's one-click deploy feature for Generative Engine Optimization.

score 58 · 22,175 chars context · 594 in-tokens

HTML answered using 22175 chars of layer context (minimum fed this run).

Schema

partial

This page is about AigeoRadar, an AI visibility and GEO platform that helps with website optimization and discovery assets.

score 50 · 15,629 chars context · 517 in-tokens

Schema was only partially aligned with gold from 15629 chars of context.

AIPM

sufficient

This page is about WordPress GEO discovery with llms.txt and aims to publish discovery assets for AI visibility using AigeoRadar.

score 69 · 35,339 chars context · 927 in-tokens

AIPM answered using 35339 chars of layer context (minimum fed this run).

Q2 · Understanding · orientation

What is the main service or product offered?

Gold: WordPress GEO: llms.txt, llms-full.txt, and AI discovery in 2026

Gold source: html.h1|meta|prose · html_independent

Sufficient: HTML, AIPM · Needs more / other context: Schema

Measured retrieval cost (API tokens) · lowest cost SCHEMA · 521 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML h1+title+meta_description+lead_paragraphs+prose_span+body_excerpt+time 598 api 2 medium yes
Schema name+headline+description+publisher+@type+block+dateModified+datePublished 521 api 2 low no
AIPM primaryTopic+title+purpose+abstract+publisher+keyFacts+audience+contentIntent+relatedPages+dateModified+datePublished+reviewDate+faqEntity 966 api 2 medium yes
Execution chain · AIPM · 2 round(s)
  1. Question: What is the main service or product offered?
  2. Planner: Planner mapped question `main_service` on AIPM to minimal fields: primaryTopic, title, purpose.
  3. Slice: primaryTopic+title+purpose+abstract+publisher+keyFacts+audience+contentIntent+relatedPages+dateModified+datePublished+reviewDate+faqEntity — AIPM.primaryTopic: WordPress GEO discovery with llms.txt AIPM.title: WordPress GEO: llms.txt and AI...
  4. Actual input tokens: 966
  5. LLM answer: The main service offered is the publishing of llms.txt and llms-full.txt on WordPress for AI discovery visibility.
  6. Evidence: AIPM.primaryTopic: WordPress GEO discovery with llms.txt AIPM.title: WordPress GEO: llms.txt and AI Discovery Guide | AigeoRadar AIPM.purp...
  7. Confidence: medium · matched yes

Round 1: 167 in-tok · score 50 · miss

Round 2: 799 in-tok · score 69 · match

Execution chain · HTML · 2 round(s)
  1. Question: What is the main service or product offered?
  2. Planner: Planner mapped question `main_service` on HTML to minimal fields: h1, title.
  3. Slice: h1+title+meta_description+lead_paragraphs+prose_span+body_excerpt+time — HTML.h1: WordPress GEO: llms.txt, llms-full.txt, and AI discovery in 2026 HTML.title: WordPress GEO...
  4. Actual input tokens: 598
  5. LLM answer: The main service offered is a guide for WordPress owners to publish llms.txt and related AI discovery files using AigeoRadar's one-click deploy.
  6. Evidence: HTML.h1: WordPress GEO: llms.txt, llms-full.txt, and AI discovery in 2026 HTML.title: WordPress GEO: llms.txt and AI Discovery Guide | Aige...
  7. Confidence: medium · matched yes

Round 1: 145 in-tok · score 0 · miss

Round 2: 453 in-tok · score 58 · match

Execution chain · SCHEMA · 2 round(s)
  1. Question: What is the main service or product offered?
  2. Planner: Planner mapped question `main_service` on SCHEMA to minimal fields: name, headline, description.
  3. Slice: name+headline+description+publisher+@type+block+dateModified+datePublished — SCHEMA.name: AigeoRadar SCHEMA.headline: WordPress GEO: llms.txt and AI Discovery Guide | AigeoRada...
  4. Actual input tokens: 521
  5. LLM answer: AigeoRadar offers an AI visibility and GEO (Generative Engine Optimization) platform that crawls websites and generates fixes.
  6. Evidence: SCHEMA.name: AigeoRadar SCHEMA.headline: WordPress GEO: llms.txt and AI Discovery Guide | AigeoRadar SCHEMA.description: Explains the Word...
  7. Confidence: low · matched no

Round 1: 170 in-tok · score 0 · miss

Round 2: 351 in-tok · score 23 · miss

HTML

sufficient

The main service offered is a guide for WordPress owners to publish llms.txt and related AI discovery files using AigeoRadar's one-click deploy.

score 58 · 22,175 chars context · 598 in-tokens

HTML answered using 22175 chars of layer context (minimum fed this run).

Schema

insufficient

AigeoRadar offers an AI visibility and GEO (Generative Engine Optimization) platform that crawls websites and generates fixes.

score 23 · 15,629 chars context · 521 in-tokens

Schema did not answer from 15629 chars of context — additional or different layer context needed.

AIPM

sufficient

The main service offered is the publishing of llms.txt and llms-full.txt on WordPress for AI discovery visibility.

score 69 · 35,339 chars context · 966 in-tokens

AIPM answered using 35339 chars of layer context (minimum fed this run).

Q3 · Understanding · orientation

Who is the publisher or brand?

Gold: AigeoRadar

Gold source: html.jsonld|title · html_independent

Sufficient: HTML, Schema, AIPM

Measured retrieval cost (API tokens) · lowest cost AIPM · 103 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML title 118 api 1 high yes
Schema publisher+name 111 api 1 high yes
AIPM publisher 103 api 1 high yes
Execution chain · AIPM · 1 round(s)
  1. Question: Who is the publisher or brand?
  2. Planner: Planner mapped question `publisher` on AIPM to minimal fields: publisher.
  3. Slice: publisher — AIPM.publisher: AigeoRadar
  4. Actual input tokens: 103
  5. LLM answer: AigeoRadar.
  6. Evidence: AIPM.publisher: AigeoRadar
  7. Confidence: high · matched yes
Execution chain · HTML · 1 round(s)
  1. Question: Who is the publisher or brand?
  2. Planner: Planner mapped question `publisher` on HTML to minimal fields: title.
  3. Slice: title — HTML.title: WordPress GEO: llms.txt and AI Discovery Guide | AigeoRadar — AigeoRadar
  4. Actual input tokens: 118
  5. LLM answer: AigeoRadar.
  6. Evidence: HTML.title: WordPress GEO: llms.txt and AI Discovery Guide | AigeoRadar — AigeoRadar
  7. Confidence: high · matched yes
Execution chain · SCHEMA · 1 round(s)
  1. Question: Who is the publisher or brand?
  2. Planner: Planner mapped question `publisher` on SCHEMA to minimal fields: publisher, name.
  3. Slice: publisher+name — SCHEMA.publisher: AigeoRadar SCHEMA.name: AigeoRadar
  4. Actual input tokens: 111
  5. LLM answer: AigeoRadar.
  6. Evidence: SCHEMA.publisher: AigeoRadar SCHEMA.name: AigeoRadar
  7. Confidence: high · matched yes

HTML

sufficient

AigeoRadar.

score 100 · 22,175 chars context · 118 in-tokens

HTML answered using 22175 chars of layer context (minimum fed this run).

Schema

sufficient

AigeoRadar.

score 100 · 15,629 chars context · 111 in-tokens

Schema answered using 15629 chars of layer context (minimum fed this run).

AIPM

sufficient

AigeoRadar.

score 100 · 35,339 chars context · 103 in-tokens

AIPM answered using 35339 chars of layer context (minimum fed this run).

Q4 · Retrieval · depth

Where does this business operate or which city is mentioned?

Gold: UNKNOWN

Gold source: html.address|prose|jsonld · html_independent

Sufficient: HTML, Schema, AIPM

Measured retrieval cost (API tokens) · lowest cost SCHEMA · 314 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML prose_span+lead_paragraphs+title+h1+meta_description+body_excerpt+time 616 api 2 low yes
Schema name+headline+description+publisher+@type+block+dateModified+datePublished 314 api 2 low yes
AIPM purpose+abstract+primaryTopic+title+publisher+keyFacts+audience+contentIntent+relatedPages+dateModified+datePublished+reviewDate+faqEntity 801 api 2 low yes
Execution chain · AIPM · 2 round(s)
  1. Question: Where does this business operate or which city is mentioned?
  2. Planner: Planner mapped question `geo_signal` on AIPM to minimal fields: geo, areaServed, address.
  3. Slice: purpose+abstract+primaryTopic+title+publisher+keyFacts+audience+contentIntent+relatedPages+dateModified+datePublished+reviewDate+faqEntity — AIPM.purpose: Publish llms.txt and llms-full.txt on WordPress with AigeoRadar for ChatGPT, Perplexit...
  4. Actual input tokens: 801
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 0 in-tok · score 0 · miss

Round 2: 801 in-tok · score 100 · match

Execution chain · HTML · 2 round(s)
  1. Question: Where does this business operate or which city is mentioned?
  2. Planner: Planner mapped question `geo_signal` on HTML to minimal fields: prose_span, lead_paragraphs.
  3. Slice: prose_span+lead_paragraphs+title+h1+meta_description+body_excerpt+time — HTML.prose_span: UNKNOWN HTML.lead_paragraphs: This guide shows WordPress owners how to publish llm...
  4. Actual input tokens: 616
  5. LLM answer: UNKNOWN
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 180 in-tok · score 100 · match

Round 2: 436 in-tok · score 100 · match

Execution chain · SCHEMA · 2 round(s)
  1. Question: Where does this business operate or which city is mentioned?
  2. Planner: Planner mapped question `geo_signal` on SCHEMA to minimal fields: address, addressLocality, areaServed.
  3. Slice: name+headline+description+publisher+@type+block+dateModified+datePublished — SCHEMA.name: AigeoRadar SCHEMA.headline: WordPress GEO: llms.txt and AI Discovery Guide | AigeoRada...
  4. Actual input tokens: 314
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 0 in-tok · score 0 · miss

Round 2: 314 in-tok · score 100 · match

HTML

sufficient

UNKNOWN

score 100 · 22,175 chars context · 616 in-tokens

HTML answered using 22175 chars of layer context (minimum fed this run).

Schema

sufficient

UNKNOWN.

score 100 · 15,629 chars context · 314 in-tokens

Schema answered using 15629 chars of layer context (minimum fed this run).

AIPM

sufficient

UNKNOWN.

score 100 · 35,339 chars context · 801 in-tokens

AIPM answered using 35339 chars of layer context (minimum fed this run).

Q5 · Understanding · orientation

Who is the intended audience of this page?

Gold: businesses

Gold source: html.prose|meta · html_independent

Sufficient: AIPM · Needs more / other context: HTML, Schema

Measured retrieval cost (API tokens) · lowest cost AIPM · 163 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML meta_description+lead_paragraphs+title+h1+prose_span+body_excerpt+time 644 api 2 low no
Schema description+name+headline+publisher+@type+block+dateModified+datePublished 499 api 2 low no
AIPM audience+purpose 163 api 1 high yes
Execution chain · AIPM · 1 round(s)
  1. Question: Who is the intended audience of this page?
  2. Planner: Planner mapped question `audience` on AIPM to minimal fields: audience, purpose.
  3. Slice: audience+purpose — AIPM.audience: ["businesses","website owners","WordPress site owners","SEO professionals","digital a...
  4. Actual input tokens: 163
  5. LLM answer: The intended audience of this page includes businesses, website owners, WordPress site owners, SEO professionals, digital agencies, B2B, SMBs, and agencies.
  6. Evidence: AIPM.audience: ["businesses","website owners","WordPress site owners","SEO professionals","digital agencies","B2B","SMBs","agency"] AIPM.pu...
  7. Confidence: high · matched yes
Execution chain · HTML · 2 round(s)
  1. Question: Who is the intended audience of this page?
  2. Planner: Planner mapped question `audience` on HTML to minimal fields: meta_description, lead_paragraphs.
  3. Slice: meta_description+lead_paragraphs+title+h1+prose_span+body_excerpt+time — HTML.meta_description: Publish llms.txt and llms-full.txt on WordPress with AigeoRadar. One-click GE...
  4. Actual input tokens: 644
  5. LLM answer: The intended audience of this page is WordPress owners.
  6. Evidence: HTML.meta_description: Publish llms.txt and llms-full.txt on WordPress with AigeoRadar. One-click GEO deploy for ChatGPT, Perplexity, and Ge...
  7. Confidence: low · matched no

Round 1: 209 in-tok · score 0 · miss

Round 2: 435 in-tok · score 0 · miss

Execution chain · SCHEMA · 2 round(s)
  1. Question: Who is the intended audience of this page?
  2. Planner: Planner mapped question `audience` on SCHEMA to minimal fields: description, name.
  3. Slice: description+name+headline+publisher+@type+block+dateModified+datePublished — SCHEMA.description: Explains the WordPress GEO publishing workflow: which discovery assets to ship,...
  4. Actual input tokens: 499
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched no

Round 1: 148 in-tok · score 0 · miss

Round 2: 351 in-tok · score 0 · miss

HTML

insufficient

The intended audience of this page is WordPress owners.

score 0 · 22,175 chars context · 644 in-tokens

HTML did not answer from 22175 chars of context — additional or different layer context needed.

Schema

insufficient

UNKNOWN.

score 0 · 15,629 chars context · 499 in-tokens

Schema did not answer from 15629 chars of context — additional or different layer context needed.

AIPM

sufficient

The intended audience of this page includes businesses, website owners, WordPress site owners, SEO professionals, digital agencies, B2B, SMBs, and agencies.

score 88 · 35,339 chars context · 163 in-tokens

AIPM answered using 35339 chars of layer context (minimum fed this run).

Q6 · Understanding · orientation

What is the purpose of this page?

Gold: Publish llms.txt and llms-full.txt on WordPress with AigeoRadar. One-click GEO deploy for ChatGPT, Perplexity, and Gemini visibility.

Gold source: html.page_summary · html_independent

Sufficient: HTML, AIPM · Needs more / other context: Schema

Measured retrieval cost (API tokens) · lowest cost AIPM · 129 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML meta_description+lead_paragraphs 208 api 1 medium yes
Schema description+name+headline+publisher+@type+block+dateModified+datePublished 458 api 2 low no
AIPM purpose 129 api 1 medium yes
Execution chain · AIPM · 1 round(s)
  1. Question: What is the purpose of this page?
  2. Planner: Planner mapped question `purpose` on AIPM to minimal fields: purpose.
  3. Slice: purpose — AIPM.purpose: Publish llms.txt and llms-full.txt on WordPress with AigeoRadar for ChatGPT, Perplexit...
  4. Actual input tokens: 129
  5. LLM answer: The purpose of this page is to publish llms.txt and llms-full.txt on WordPress for visibility of ChatGPT, Perplexity, and Gemini.
  6. Evidence: AIPM.purpose: Publish llms.txt and llms-full.txt on WordPress with AigeoRadar for ChatGPT, Perplexity, and Gemini visibility.
  7. Confidence: medium · matched yes
Execution chain · HTML · 1 round(s)
  1. Question: What is the purpose of this page?
  2. Planner: Planner mapped question `purpose` on HTML to minimal fields: meta_description, lead_paragraphs.
  3. Slice: meta_description+lead_paragraphs — HTML.meta_description: Publish llms.txt and llms-full.txt on WordPress with AigeoRadar. One-click GE...
  4. Actual input tokens: 208
  5. LLM answer: The purpose of this page is to guide WordPress owners on how to publish llms.txt and related AI discovery files using AigeoRadar for improved AI visibility.
  6. Evidence: HTML.meta_description: Publish llms.txt and llms-full.txt on WordPress with AigeoRadar. One-click GEO deploy for ChatGPT, Perplexity, and Ge...
  7. Confidence: medium · matched yes
Execution chain · SCHEMA · 2 round(s)
  1. Question: What is the purpose of this page?
  2. Planner: Planner mapped question `purpose` on SCHEMA to minimal fields: description, name.
  3. Slice: description+name+headline+publisher+@type+block+dateModified+datePublished — SCHEMA.description: Explains the WordPress GEO publishing workflow: which discovery assets to ship,...
  4. Actual input tokens: 458
  5. LLM answer: The purpose of this page is to explain the WordPress GEO publishing workflow and related topics.
  6. Evidence: SCHEMA.description: Explains the WordPress GEO publishing workflow: which discovery assets to ship, how Connector-based deploy works after c...
  7. Confidence: low · matched no

Round 1: 147 in-tok · score 19 · miss

Round 2: 311 in-tok · score 19 · miss

HTML

sufficient

The purpose of this page is to guide WordPress owners on how to publish llms.txt and related AI discovery files using AigeoRadar for improved AI visibility.

score 70 · 22,175 chars context · 208 in-tokens

HTML answered using 22175 chars of layer context (minimum fed this run).

Schema

insufficient

The purpose of this page is to explain the WordPress GEO publishing workflow and related topics.

score 19 · 15,629 chars context · 458 in-tokens

Schema did not answer from 15629 chars of context — additional or different layer context needed.

AIPM

sufficient

The purpose of this page is to publish llms.txt and llms-full.txt on WordPress for visibility of ChatGPT, Perplexity, and Gemini.

score 78 · 35,339 chars context · 129 in-tokens

AIPM answered using 35339 chars of layer context (minimum fed this run).

Q7 · Understanding · orientation

Summarize this page in one sentence.

Gold: Publish llms.txt and llms-full.txt on WordPress with AigeoRadar. One-click GEO deploy for ChatGPT, Perplexity, and Gemini visibility.

Gold source: html.page_summary · html_independent

Sufficient: HTML · Needs more / other context: Schema, AIPM

Measured retrieval cost (API tokens) · lowest cost HTML · 209 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML meta_description+lead_paragraphs 209 api 1 medium yes
Schema description+name 460 api 2 low no
AIPM abstract+purpose+primaryTopic+title+publisher+keyFacts+audience+contentIntent+relatedPages+dateModified+datePublished+reviewDate+faqEntity 974 api 2 low no
Execution chain · AIPM · 2 round(s)
  1. Question: Summarize this page in one sentence.
  2. Planner: Planner mapped question `abstract` on AIPM to minimal fields: abstract, purpose.
  3. Slice: abstract+purpose+primaryTopic+title+publisher+keyFacts+audience+contentIntent+relatedPages+dateModified+datePublished+reviewDate+faqEntity — AIPM.abstract: Explains the WordPress GEO publishing workflow: which discovery assets to ship, how C...
  4. Actual input tokens: 974
  5. LLM answer: The page explains the WordPress GEO publishing workflow and the use of discovery assets like llms.txt for enhancing AI visibility on WordPress sites.
  6. Evidence: AIPM.abstract: Explains the WordPress GEO publishing workflow: which discovery assets to ship, how Connector-based deploy works after crawl,...
  7. Confidence: low · matched no

Round 1: 175 in-tok · score 26 · miss

Round 2: 799 in-tok · score 35 · miss

Execution chain · HTML · 1 round(s)
  1. Question: Summarize this page in one sentence.
  2. Planner: Planner mapped question `abstract` on HTML to minimal fields: meta_description, lead_paragraphs.
  3. Slice: meta_description+lead_paragraphs — HTML.meta_description: Publish llms.txt and llms-full.txt on WordPress with AigeoRadar. One-click GE...
  4. Actual input tokens: 209
  5. LLM answer: This guide explains how WordPress owners can publish llms.txt and related AI discovery files using AigeoRadar for improved AI visibility.
  6. Evidence: HTML.meta_description: Publish llms.txt and llms-full.txt on WordPress with AigeoRadar. One-click GEO deploy for ChatGPT, Perplexity, and Ge...
  7. Confidence: medium · matched yes
Execution chain · SCHEMA · 2 round(s)
  1. Question: Summarize this page in one sentence.
  2. Planner: Planner mapped question `abstract` on SCHEMA to minimal fields: description, name.
  3. Slice: description+name — SCHEMA.description: Explains the WordPress GEO publishing workflow: which discovery assets to ship,...
  4. Actual input tokens: 460
  5. LLM answer: The page explains the WordPress GEO publishing workflow, including discovery assets, Connector-based deployment, the Starter plan for one site, and the relevanc...
  6. Evidence: SCHEMA.description: Explains the WordPress GEO publishing workflow: which discovery assets to ship, how Connector-based deploy works after c...
  7. Confidence: low · matched no

Round 1: 148 in-tok · score 20 · miss

Round 2: 312 in-tok · score 14 · miss

Root cause · Layer miss

Improve orientation fields; do not paste full HTML into the sidecar.

HTML

sufficient

This guide explains how WordPress owners can publish llms.txt and related AI discovery files using AigeoRadar for improved AI visibility.

score 70 · 22,175 chars context · 209 in-tokens

HTML answered using 22175 chars of layer context (minimum fed this run).

Schema

insufficient

The page explains the WordPress GEO publishing workflow, including discovery assets, Connector-based deployment, the Starter plan for one site, and the relevance of Shopify GEO compared to WordPress.

score 20 · 15,629 chars context · 460 in-tokens

Schema did not answer from 15629 chars of context — additional or different layer context needed.

AIPM

insufficient

The page explains the WordPress GEO publishing workflow and the use of discovery assets like llms.txt for enhancing AI visibility on WordPress sites.

score 35 · 35,339 chars context · 974 in-tokens

AIPM did not answer from 35339 chars of context — additional or different layer context needed.

Q8 · Understanding · orientation

What is the primary topic or headline of this page?

Gold: WordPress GEO: llms.txt, llms-full.txt, and AI discovery in 2026

Gold source: html.h1|title · html_independent

Sufficient: HTML, Schema, AIPM

Measured retrieval cost (API tokens) · lowest cost HTML · 147 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML h1+title 147 api 1 high yes
Schema name+headline+description 172 api 1 medium yes
AIPM primaryTopic+title+purpose 169 api 1 medium yes
Execution chain · AIPM · 1 round(s)
  1. Question: What is the primary topic or headline of this page?
  2. Planner: Planner mapped question `primary_topic` on AIPM to minimal fields: primaryTopic, title, purpose.
  3. Slice: primaryTopic+title+purpose — AIPM.primaryTopic: WordPress GEO discovery with llms.txt AIPM.title: WordPress GEO: llms.txt and AI...
  4. Actual input tokens: 169
  5. LLM answer: WordPress GEO discovery with llms.txt
  6. Evidence: WordPress GEO discovery with llms.txt
  7. Confidence: medium · matched yes
Execution chain · HTML · 1 round(s)
  1. Question: What is the primary topic or headline of this page?
  2. Planner: Planner mapped question `primary_topic` on HTML to minimal fields: h1, title.
  3. Slice: h1+title — HTML.h1: WordPress GEO: llms.txt, llms-full.txt, and AI discovery in 2026 HTML.title: WordPress GEO...
  4. Actual input tokens: 147
  5. LLM answer: The primary topic or headline of this page is "WordPress GEO: llms.txt, llms-full.txt, and AI discovery in 2026."
  6. Evidence: HTML.h1: WordPress GEO: llms.txt, llms-full.txt, and AI discovery in 2026 HTML.title: WordPress GEO: llms.txt and AI Discovery Guide | Aige...
  7. Confidence: high · matched yes
Execution chain · SCHEMA · 1 round(s)
  1. Question: What is the primary topic or headline of this page?
  2. Planner: Planner mapped question `primary_topic` on SCHEMA to minimal fields: name, headline, description.
  3. Slice: name+headline+description — SCHEMA.name: AigeoRadar SCHEMA.headline: WordPress GEO: llms.txt and AI Discovery Guide | AigeoRada...
  4. Actual input tokens: 172
  5. LLM answer: The primary topic or headline of this page is "WordPress GEO: llms.txt and AI Discovery Guide | AigeoRadar."
  6. Evidence: SCHEMA.name: AigeoRadar SCHEMA.headline: WordPress GEO: llms.txt and AI Discovery Guide | AigeoRadar SCHEMA.description: Explains the Word...
  7. Confidence: medium · matched yes

HTML

sufficient

The primary topic or headline of this page is "WordPress GEO: llms.txt, llms-full.txt, and AI discovery in 2026."

score 87 · 22,175 chars context · 147 in-tokens

HTML answered using 22175 chars of layer context (minimum fed this run).

Schema

sufficient

The primary topic or headline of this page is "WordPress GEO: llms.txt and AI Discovery Guide | AigeoRadar."

score 69 · 15,629 chars context · 172 in-tokens

Schema answered using 15629 chars of layer context (minimum fed this run).

AIPM

sufficient

WordPress GEO discovery with llms.txt

score 77 · 35,339 chars context · 169 in-tokens

AIPM answered using 35339 chars of layer context (minimum fed this run).

Q9 · Metadata · orientation

What language is this page written in?

Gold: en

Gold source: html.lang · html_independent

Sufficient: HTML, Schema, AIPM

Measured retrieval cost (API tokens) · lowest cost HTML · 100 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML lang 100 api 1 high yes
Schema inLanguage 101 api 1 high yes
AIPM inLanguage 102 api 1 high yes
Execution chain · AIPM · 1 round(s)
  1. Question: What language is this page written in?
  2. Planner: Planner mapped question `language` on AIPM to minimal fields: inLanguage.
  3. Slice: inLanguage — AIPM.inLanguage: en
  4. Actual input tokens: 102
  5. LLM answer: The page is written in English.
  6. Evidence: AIPM.inLanguage: en
  7. Confidence: high · matched yes
Execution chain · HTML · 1 round(s)
  1. Question: What language is this page written in?
  2. Planner: Planner mapped question `language` on HTML to minimal fields: lang.
  3. Slice: lang — HTML.lang: lang=en
  4. Actual input tokens: 100
  5. LLM answer: English
  6. Evidence: HTML.lang: lang=en
  7. Confidence: high · matched yes
Execution chain · SCHEMA · 1 round(s)
  1. Question: What language is this page written in?
  2. Planner: Planner mapped question `language` on SCHEMA to minimal fields: inLanguage.
  3. Slice: inLanguage — SCHEMA.inLanguage: en
  4. Actual input tokens: 101
  5. LLM answer: The page is written in English.
  6. Evidence: SCHEMA.inLanguage: en
  7. Confidence: high · matched yes

HTML

sufficient

English

score 100 · 22,175 chars context · 100 in-tokens

HTML answered using 22175 chars of layer context (minimum fed this run).

Schema

sufficient

The page is written in English.

score 88 · 15,629 chars context · 101 in-tokens

Schema answered using 15629 chars of layer context (minimum fed this run).

AIPM

sufficient

The page is written in English.

score 88 · 35,339 chars context · 102 in-tokens

AIPM answered using 35339 chars of layer context (minimum fed this run).

Q10 · Retrieval · depth

What phone number or contact detail is listed?

Gold: UNKNOWN

Gold source: html.title|prose|jsonld.telephone · html_independent

Sufficient: HTML, Schema, AIPM

Measured retrieval cost (API tokens) · lowest cost SCHEMA · 553 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML title+lead_paragraphs+h1+meta_description+prose_span+body_excerpt+time 630 api 2 low yes
Schema block+description+name+headline+publisher+@type+dateModified+datePublished 553 api 2 low yes
AIPM keyFacts+publisher+abstract+purpose+primaryTopic+title+audience+contentIntent+relatedPages+dateModified+datePublished+reviewDate+faqEntity 1,034 api 2 low yes
Execution chain · AIPM · 2 round(s)
  1. Question: What phone number or contact detail is listed?
  2. Planner: Planner mapped question `contact_phone` on AIPM to minimal fields: keyFacts, publisher, abstract.
  3. Slice: keyFacts+publisher+abstract+purpose+primaryTopic+title+audience+contentIntent+relatedPages+dateModified+datePublished+reviewDate+faqEntity — AIPM.keyFacts: GEO (Generative Engine Optimization) is the layer after SEO that uses machine-readabl...
  4. Actual input tokens: 1,034
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 235 in-tok · score 100 · match

Round 2: 799 in-tok · score 100 · match

Execution chain · HTML · 2 round(s)
  1. Question: What phone number or contact detail is listed?
  2. Planner: Planner mapped question `contact_phone` on HTML to minimal fields: title, lead_paragraphs.
  3. Slice: title+lead_paragraphs+h1+meta_description+prose_span+body_excerpt+time — HTML.title: WordPress GEO: llms.txt and AI Discovery Guide | AigeoRadar — AigeoRadar HTML.lead_para...
  4. Actual input tokens: 630
  5. LLM answer: UNKNOWN
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 196 in-tok · score 100 · match

Round 2: 434 in-tok · score 100 · match

Execution chain · SCHEMA · 2 round(s)
  1. Question: What phone number or contact detail is listed?
  2. Planner: Planner mapped question `contact_phone` on SCHEMA to minimal fields: telephone, block, description.
  3. Slice: block+description+name+headline+publisher+@type+dateModified+datePublished — SCHEMA.block: { "@context": "https://schema.org", "@graph": [ { "@type":...
  4. Actual input tokens: 553
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 241 in-tok · score 100 · match

Round 2: 312 in-tok · score 100 · match

HTML

sufficient

UNKNOWN

score 100 · 22,175 chars context · 630 in-tokens

HTML answered using 22175 chars of layer context (minimum fed this run).

Schema

sufficient

UNKNOWN.

score 100 · 15,629 chars context · 553 in-tokens

Schema answered using 15629 chars of layer context (minimum fed this run).

AIPM

sufficient

UNKNOWN.

score 100 · 35,339 chars context · 1,034 in-tokens

AIPM answered using 35339 chars of layer context (minimum fed this run).

Q11 · Retrieval · depth

What physical address is listed for this business?

Gold: UNKNOWN

Gold source: html.prose|jsonld.address · html_independent

Sufficient: HTML, Schema, AIPM

Measured retrieval cost (API tokens) · lowest cost SCHEMA · 312 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML prose_span+lead_paragraphs+title+h1+meta_description+body_excerpt+time 612 api 2 low yes
Schema name+headline+description+publisher+@type+block+dateModified+datePublished 312 api 2 low yes
AIPM purpose+abstract+primaryTopic+title+publisher+keyFacts+audience+contentIntent+relatedPages+dateModified+datePublished+reviewDate+faqEntity 799 api 2 low yes
Execution chain · AIPM · 2 round(s)
  1. Question: What physical address is listed for this business?
  2. Planner: Planner mapped question `address` on AIPM to minimal fields: address, geo, areaServed.
  3. Slice: purpose+abstract+primaryTopic+title+publisher+keyFacts+audience+contentIntent+relatedPages+dateModified+datePublished+reviewDate+faqEntity — AIPM.purpose: Publish llms.txt and llms-full.txt on WordPress with AigeoRadar for ChatGPT, Perplexit...
  4. Actual input tokens: 799
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 0 in-tok · score 0 · miss

Round 2: 799 in-tok · score 100 · match

Execution chain · HTML · 2 round(s)
  1. Question: What physical address is listed for this business?
  2. Planner: Planner mapped question `address` on HTML to minimal fields: prose_span, lead_paragraphs.
  3. Slice: prose_span+lead_paragraphs+title+h1+meta_description+body_excerpt+time — HTML.prose_span: UNKNOWN HTML.lead_paragraphs: This guide shows WordPress owners how to publish llm...
  4. Actual input tokens: 612
  5. LLM answer: UNKNOWN
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 178 in-tok · score 100 · match

Round 2: 434 in-tok · score 100 · match

Execution chain · SCHEMA · 2 round(s)
  1. Question: What physical address is listed for this business?
  2. Planner: Planner mapped question `address` on SCHEMA to minimal fields: address, addressLocality, areaServed.
  3. Slice: name+headline+description+publisher+@type+block+dateModified+datePublished — SCHEMA.name: AigeoRadar SCHEMA.headline: WordPress GEO: llms.txt and AI Discovery Guide | AigeoRada...
  4. Actual input tokens: 312
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 0 in-tok · score 0 · miss

Round 2: 312 in-tok · score 100 · match

HTML

sufficient

UNKNOWN

score 100 · 22,175 chars context · 612 in-tokens

HTML answered using 22175 chars of layer context (minimum fed this run).

Schema

sufficient

UNKNOWN.

score 100 · 15,629 chars context · 312 in-tokens

Schema answered using 15629 chars of layer context (minimum fed this run).

AIPM

sufficient

UNKNOWN.

score 100 · 35,339 chars context · 799 in-tokens

AIPM answered using 35339 chars of layer context (minimum fed this run).

Q12 · Retrieval · depth

What are the opening hours or availability?

Gold: UNKNOWN

Gold source: html.prose|jsonld.openingHours · html_independent

Sufficient: HTML, Schema, AIPM

Measured retrieval cost (API tokens) · lowest cost SCHEMA · 551 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML lead_paragraphs+body_excerpt+prose_span+title+h1+meta_description+time 768 api 2 low yes
Schema block+description+name+headline+publisher+@type+dateModified+datePublished 551 api 2 low yes
AIPM keyFacts+abstract+purpose+primaryTopic+title+publisher+audience+contentIntent+relatedPages+dateModified+datePublished+reviewDate+faqEntity 1,057 api 2 low yes
Execution chain · AIPM · 2 round(s)
  1. Question: What are the opening hours or availability?
  2. Planner: Planner mapped question `hours` on AIPM to minimal fields: keyFacts, abstract, purpose.
  3. Slice: keyFacts+abstract+purpose+primaryTopic+title+publisher+audience+contentIntent+relatedPages+dateModified+datePublished+reviewDate+faqEntity — AIPM.keyFacts: GEO (Generative Engine Optimization) is the layer after SEO that uses machine-readabl...
  4. Actual input tokens: 1,057
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 259 in-tok · score 100 · match

Round 2: 798 in-tok · score 100 · match

Execution chain · HTML · 2 round(s)
  1. Question: What are the opening hours or availability?
  2. Planner: Planner mapped question `hours` on HTML to minimal fields: lead_paragraphs, body_excerpt, prose_span.
  3. Slice: lead_paragraphs+body_excerpt+prose_span+title+h1+meta_description+time — HTML.lead_paragraphs: This guide shows WordPress owners how to publish llms.txt and related AI disco...
  4. Actual input tokens: 768
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 335 in-tok · score 100 · match

Round 2: 433 in-tok · score 100 · match

Execution chain · SCHEMA · 2 round(s)
  1. Question: What are the opening hours or availability?
  2. Planner: Planner mapped question `hours` on SCHEMA to minimal fields: openingHours, block, description.
  3. Slice: block+description+name+headline+publisher+@type+dateModified+datePublished — SCHEMA.block: { "@context": "https://schema.org", "@graph": [ { "@type":...
  4. Actual input tokens: 551
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 240 in-tok · score 100 · match

Round 2: 311 in-tok · score 100 · match

HTML

sufficient

UNKNOWN.

score 100 · 22,175 chars context · 768 in-tokens

HTML answered using 22175 chars of layer context (minimum fed this run).

Schema

sufficient

UNKNOWN.

score 100 · 15,629 chars context · 551 in-tokens

Schema answered using 15629 chars of layer context (minimum fed this run).

AIPM

sufficient

UNKNOWN.

score 100 · 35,339 chars context · 1,057 in-tokens

AIPM answered using 35339 chars of layer context (minimum fed this run).

Q13 · Retrieval · depth

Is pricing or package information mentioned, and what is it?

Gold: $9.90/mo

Gold source: html.prose|jsonld.offers · html_independent

No layer provided a sufficient answer from its context alone.

Measured retrieval cost (API tokens) · lowest cost SCHEMA · 559 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML lead_paragraphs+body_excerpt+prose_span+title+h1+meta_description+time 784 api 2 low no
Schema block+description+name+headline+publisher+@type+dateModified+datePublished 559 api 2 low no
AIPM keyFacts+abstract+purpose+primaryTopic+title+publisher+audience+contentIntent+relatedPages+dateModified+datePublished+reviewDate+faqEntity 1,030 api 2 low no
Execution chain · AIPM · 2 round(s)
  1. Question: Is pricing or package information mentioned, and what is it?
  2. Planner: Planner mapped question `pricing` on AIPM to minimal fields: keyFacts, sections, abstract.
  3. Slice: keyFacts+abstract+purpose+primaryTopic+title+publisher+audience+contentIntent+relatedPages+dateModified+datePublished+reviewDate+faqEntity — AIPM.keyFacts: GEO (Generative Engine Optimization) is the layer after SEO that uses machine-readabl...
  4. Actual input tokens: 1,030
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched no

Round 1: 228 in-tok · score 0 · miss

Round 2: 802 in-tok · score 0 · miss

Execution chain · HTML · 2 round(s)
  1. Question: Is pricing or package information mentioned, and what is it?
  2. Planner: Planner mapped question `pricing` on HTML to minimal fields: lead_paragraphs, body_excerpt, prose_span.
  3. Slice: lead_paragraphs+body_excerpt+prose_span+title+h1+meta_description+time — HTML.lead_paragraphs: This guide shows WordPress owners how to publish llms.txt and related AI disco...
  4. Actual input tokens: 784
  5. LLM answer: Yes, the starter plan is $9.90 per month and includes one WordPress site with plugin sync.
  6. Evidence: HTML.lead_paragraphs: This guide shows WordPress owners how to publish llms.txt and related AI discovery files, and how AigeoRadar one-click...
  7. Confidence: low · matched no

Round 1: 343 in-tok · score 13 · miss

Round 2: 441 in-tok · score 14 · miss

Execution chain · SCHEMA · 2 round(s)
  1. Question: Is pricing or package information mentioned, and what is it?
  2. Planner: Planner mapped question `pricing` on SCHEMA to minimal fields: offers, price, block, description.
  3. Slice: block+description+name+headline+publisher+@type+dateModified+datePublished — SCHEMA.block: { "@context": "https://schema.org", "@graph": [ { "@type":...
  4. Actual input tokens: 559
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched no

Round 1: 244 in-tok · score 0 · miss

Round 2: 315 in-tok · score 0 · miss

Root cause · Depth gap (expected)

Prose/detail questions belong to HTML — escalate after AIPM orientation.

HTML

insufficient

Yes, the starter plan is $9.90 per month and includes one WordPress site with plugin sync.

score 14 · 22,175 chars context · 784 in-tokens

HTML did not answer from 22175 chars of context — additional or different layer context needed.

Schema

insufficient

UNKNOWN.

score 0 · 15,629 chars context · 559 in-tokens

Schema did not answer from 15629 chars of context — additional or different layer context needed.

AIPM

insufficient

UNKNOWN.

score 0 · 35,339 chars context · 1,030 in-tokens

AIPM did not answer from 35339 chars of context — additional or different layer context needed.

Q14 · Retrieval · depth

What makes this offer different or stand out?

Gold: one-click

Gold source: html.prose|meta · html_independent

Sufficient: HTML, AIPM · Needs more / other context: Schema

Measured retrieval cost (API tokens) · lowest cost HTML · 337 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML lead_paragraphs+body_excerpt+prose_span 337 api 1 high yes
Schema description+block+name+headline+publisher+@type+dateModified+datePublished 640 api 2 low no
AIPM keyFacts+abstract+purpose+primaryTopic+title+publisher+audience+contentIntent+relatedPages+dateModified+datePublished+reviewDate+faqEntity 949 api 2 high yes
Execution chain · AIPM · 2 round(s)
  1. Question: What makes this offer different or stand out?
  2. Planner: Planner mapped question `differentiator` on AIPM to minimal fields: keyFacts, abstract, purpose.
  3. Slice: keyFacts+abstract+purpose+primaryTopic+title+publisher+audience+contentIntent+relatedPages+dateModified+datePublished+reviewDate+faqEntity — AIPM.keyFacts: AigeoRadar also offers Shopify GEO discovery deploy; WordPress is described here as t...
  4. Actual input tokens: 949
  5. LLM answer: WordPress is described as the most mature one-click integration for GEO discovery compared to Shopify.
  6. Evidence: AigeoRadar also offers Shopify GEO discovery deploy; WordPress is described here as the most mature one-click integration.
  7. Confidence: high · matched yes

Round 1: 205 in-tok · score 0 · miss

Round 2: 744 in-tok · score 88 · match

Execution chain · HTML · 1 round(s)
  1. Question: What makes this offer different or stand out?
  2. Planner: Planner mapped question `differentiator` on HTML to minimal fields: lead_paragraphs, body_excerpt, prose_span.
  3. Slice: lead_paragraphs+body_excerpt+prose_span — HTML.lead_paragraphs: This guide shows WordPress owners how to publish llms.txt and related AI disco...
  4. Actual input tokens: 337
  5. LLM answer: This offer stands out by providing a one-click deploy feature specifically for WordPress sites, along with a focus on Generative Engine Optimization (GEO) for A...
  6. Evidence: HTML.lead_paragraphs: This guide shows WordPress owners how to publish llms.txt and related AI discovery files, and how AigeoRadar one-click...
  7. Confidence: high · matched yes
Execution chain · SCHEMA · 2 round(s)
  1. Question: What makes this offer different or stand out?
  2. Planner: Planner mapped question `differentiator` on SCHEMA to minimal fields: description, block, name.
  3. Slice: description+block+name+headline+publisher+@type+dateModified+datePublished — SCHEMA.description: Explains the WordPress GEO publishing workflow: which discovery assets to ship,...
  4. Actual input tokens: 640
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched no

Round 1: 289 in-tok · score 0 · miss

Round 2: 351 in-tok · score 0 · miss

HTML

sufficient

This offer stands out by providing a one-click deploy feature specifically for WordPress sites, along with a focus on Generative Engine Optimization (GEO) for AI visibility.

score 88 · 22,175 chars context · 337 in-tokens

HTML answered using 22175 chars of layer context (minimum fed this run).

Schema

insufficient

UNKNOWN.

score 0 · 15,629 chars context · 640 in-tokens

Schema did not answer from 15629 chars of context — additional or different layer context needed.

AIPM

sufficient

WordPress is described as the most mature one-click integration for GEO discovery compared to Shopify.

score 88 · 35,339 chars context · 949 in-tokens

AIPM answered using 35339 chars of layer context (minimum fed this run).

Q15 · Evidence · depth

What trust signals are present (reviews, certificates, guarantees, years of experience)?

Gold: UNKNOWN

Gold source: html.prose|jsonld · html_independent

Sufficient: HTML, Schema, AIPM

Measured retrieval cost (API tokens) · lowest cost SCHEMA · 576 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML lead_paragraphs+body_excerpt+prose_span+title+h1+meta_description+time 784 api 2 low yes
Schema description+block+name+headline+publisher+@type+dateModified+datePublished 576 api 2 low yes
AIPM keyFacts+abstract+purpose+primaryTopic+title+publisher+audience+contentIntent+relatedPages+dateModified+datePublished+reviewDate+faqEntity 1,073 api 2 low yes
Execution chain · AIPM · 2 round(s)
  1. Question: What trust signals are present (reviews, certificates, guarantees, years of experience)?
  2. Planner: Planner mapped question `trust` on AIPM to minimal fields: keyFacts, abstract, purpose.
  3. Slice: keyFacts+abstract+purpose+primaryTopic+title+publisher+audience+contentIntent+relatedPages+dateModified+datePublished+reviewDate+faqEntity — AIPM.keyFacts: GEO (Generative Engine Optimization) is the layer after SEO that uses machine-readabl...
  4. Actual input tokens: 1,073
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 267 in-tok · score 100 · match

Round 2: 806 in-tok · score 100 · match

Execution chain · HTML · 2 round(s)
  1. Question: What trust signals are present (reviews, certificates, guarantees, years of experience)?
  2. Planner: Planner mapped question `trust` on HTML to minimal fields: lead_paragraphs, body_excerpt, prose_span.
  3. Slice: lead_paragraphs+body_excerpt+prose_span+title+h1+meta_description+time — HTML.lead_paragraphs: This guide shows WordPress owners how to publish llms.txt and related AI disco...
  4. Actual input tokens: 784
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 343 in-tok · score 100 · match

Round 2: 441 in-tok · score 100 · match

Execution chain · SCHEMA · 2 round(s)
  1. Question: What trust signals are present (reviews, certificates, guarantees, years of experience)?
  2. Planner: Planner mapped question `trust` on SCHEMA to minimal fields: description, block, name.
  3. Slice: description+block+name+headline+publisher+@type+dateModified+datePublished — SCHEMA.description: Explains the WordPress GEO publishing workflow: which discovery assets to ship,...
  4. Actual input tokens: 576
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 257 in-tok · score 100 · match

Round 2: 319 in-tok · score 100 · match

HTML

sufficient

UNKNOWN.

score 100 · 22,175 chars context · 784 in-tokens

HTML answered using 22175 chars of layer context (minimum fed this run).

Schema

sufficient

UNKNOWN.

score 100 · 15,629 chars context · 576 in-tokens

Schema answered using 15629 chars of layer context (minimum fed this run).

AIPM

sufficient

UNKNOWN.

score 100 · 35,339 chars context · 1,073 in-tokens

AIPM answered using 35339 chars of layer context (minimum fed this run).

Q16 · Retrieval · depth

What frequently asked questions or FAQ topics are covered?

Gold: What steps can I take to monitor my GEO score on a day-to-day basis?; What common mistakes should I avoid while using AigeoRadar?; What are the plan limits for AigeoRadar?

Gold source: html.faq|jsonld.FAQPage · html_independent

Sufficient: HTML · Needs more / other context: Schema, AIPM

Measured retrieval cost (API tokens) · lowest cost HTML · 378 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML lead_paragraphs+body_excerpt+prose_span 378 api 1 medium yes
Schema block+description+name+headline+publisher+@type+dateModified+datePublished 633 api 2 low no
AIPM faqEntity+keyFacts+purpose+abstract+primaryTopic+title+publisher+audience+contentIntent+relatedPages+dateModified+datePublished+reviewDate 1,234 api 2 low no
Execution chain · AIPM · 2 round(s)
  1. Question: What frequently asked questions or FAQ topics are covered?
  2. Planner: Planner mapped question `faq` on AIPM to minimal fields: faqEntity, keyFacts.
  3. Slice: faqEntity+keyFacts+purpose+abstract+primaryTopic+title+publisher+audience+contentIntent+relatedPages+dateModified+datePublished+reviewDate — AIPM.faqEntity: [{"name":"What files should I publish for WordPress GEO?","@type":"Question","accept...
  4. Actual input tokens: 1,234
  5. LLM answer: The FAQ topics covered include publishing files for WordPress GEO, deploying AI discovery files to WordPress with AigeoRadar, the importance of llms-full.txt, a...
  6. Evidence: AIPM.faqEntity: [{"name":"What files should I publish for WordPress GEO?","@type":"Question","acceptedAnswer":{"text":"Publish llms.txt, llm...
  7. Confidence: low · matched no

Round 1: 434 in-tok · score 11 · miss

Round 2: 800 in-tok · score 11 · miss

Execution chain · HTML · 1 round(s)
  1. Question: What frequently asked questions or FAQ topics are covered?
  2. Planner: Planner mapped question `faq` on HTML to minimal fields: lead_paragraphs, body_excerpt, prose_span.
  3. Slice: lead_paragraphs+body_excerpt+prose_span — HTML.lead_paragraphs: This guide shows WordPress owners how to publish llms.txt and related AI disco...
  4. Actual input tokens: 378
  5. LLM answer: The FAQ topics covered include monitoring GEO score, common mistakes to avoid while using AigeoRadar, and plan limits for AigeoRadar.
  6. Evidence: HTML.lead_paragraphs: This guide shows WordPress owners how to publish llms.txt and related AI discovery files, and how AigeoRadar one-click...
  7. Confidence: medium · matched yes
Execution chain · SCHEMA · 2 round(s)
  1. Question: What frequently asked questions or FAQ topics are covered?
  2. Planner: Planner mapped question `faq` on SCHEMA to minimal fields: block, description.
  3. Slice: block+description+name+headline+publisher+@type+dateModified+datePublished — SCHEMA.block: { "@context": "https://schema.org", "@graph": [ { "@type":...
  4. Actual input tokens: 633
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched no

Round 1: 281 in-tok · score 0 · miss

Round 2: 352 in-tok · score 0 · miss

Root cause · Depth gap (expected)

Prose/detail questions belong to HTML — escalate after AIPM orientation.

HTML

sufficient

The FAQ topics covered include monitoring GEO score, common mistakes to avoid while using AigeoRadar, and plan limits for AigeoRadar.

score 61 · 22,175 chars context · 378 in-tokens

HTML answered using 22175 chars of layer context (minimum fed this run).

Schema

insufficient

UNKNOWN.

score 0 · 15,629 chars context · 633 in-tokens

Schema did not answer from 15629 chars of context — additional or different layer context needed.

AIPM

insufficient

The FAQ topics covered include publishing files for WordPress GEO, deploying AI discovery files to WordPress with AigeoRadar, the importance of llms-full.txt, and AigeoRadar's support for Shopify.

score 11 · 35,339 chars context · 1,234 in-tokens

AIPM did not answer from 35339 chars of context — additional or different layer context needed.

Q17 · Retrieval · depth

What action should the visitor take (call, buy, contact, book)?

Gold: enter your domain

Gold source: html.cta|prose · html_independent

Sufficient: HTML · Needs more / other context: Schema, AIPM

Measured retrieval cost (API tokens) · lowest cost HTML · 344 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML lead_paragraphs+body_excerpt+prose_span 344 api 1 high yes
Schema description+block+name+headline+publisher+@type+dateModified+datePublished 574 api 2 low no
AIPM purpose+abstract+keyFacts+primaryTopic+title+publisher+audience+contentIntent+relatedPages+dateModified+datePublished+reviewDate+faqEntity 1,071 api 2 low no
Execution chain · AIPM · 2 round(s)
  1. Question: What action should the visitor take (call, buy, contact, book)?
  2. Planner: Planner mapped question `cta` on AIPM to minimal fields: purpose, abstract, keyFacts.
  3. Slice: purpose+abstract+keyFacts+primaryTopic+title+publisher+audience+contentIntent+relatedPages+dateModified+datePublished+reviewDate+faqEntity — AIPM.purpose: Publish llms.txt and llms-full.txt on WordPress with AigeoRadar for ChatGPT, Perplexit...
  4. Actual input tokens: 1,071
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched no

Round 1: 266 in-tok · score 0 · miss

Round 2: 805 in-tok · score 0 · miss

Execution chain · HTML · 1 round(s)
  1. Question: What action should the visitor take (call, buy, contact, book)?
  2. Planner: Planner mapped question `cta` on HTML to minimal fields: lead_paragraphs, body_excerpt, prose_span.
  3. Slice: lead_paragraphs+body_excerpt+prose_span — HTML.lead_paragraphs: This guide shows WordPress owners how to publish llms.txt and related AI disco...
  4. Actual input tokens: 344
  5. LLM answer: Enter your domain on aigeoradar.com for an instant AI visibility preview.
  6. Evidence: Enter your domain on aigeoradar.com for an instant AI visibility preview. No credit card required.
  7. Confidence: high · matched yes
Execution chain · SCHEMA · 2 round(s)
  1. Question: What action should the visitor take (call, buy, contact, book)?
  2. Planner: Planner mapped question `cta` on SCHEMA to minimal fields: description, block, name.
  3. Slice: description+block+name+headline+publisher+@type+dateModified+datePublished — SCHEMA.description: Explains the WordPress GEO publishing workflow: which discovery assets to ship,...
  4. Actual input tokens: 574
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched no

Round 1: 256 in-tok · score 0 · miss

Round 2: 318 in-tok · score 0 · miss

Root cause · Depth gap (expected)

Prose/detail questions belong to HTML — escalate after AIPM orientation.

HTML

sufficient

Enter your domain on aigeoradar.com for an instant AI visibility preview.

score 88 · 22,175 chars context · 344 in-tokens

HTML answered using 22175 chars of layer context (minimum fed this run).

Schema

insufficient

UNKNOWN.

score 0 · 15,629 chars context · 574 in-tokens

Schema did not answer from 15629 chars of context — additional or different layer context needed.

AIPM

insufficient

UNKNOWN.

score 0 · 35,339 chars context · 1,071 in-tokens

AIPM did not answer from 35339 chars of context — additional or different layer context needed.

Q18 · Retrieval · depth

What related products, services, or pages are mentioned?

Gold: Features, What is AigeoRadar?, Pricing

Gold source: html.nav|prose · html_independent

Sufficient: HTML, AIPM · Needs more / other context: Schema

Measured retrieval cost (API tokens) · lowest cost AIPM · 340 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML lead_paragraphs+body_excerpt+prose_span 347 api 1 high yes
Schema description+block+name+headline+publisher+@type+dateModified+datePublished 566 api 2 low no
AIPM relatedPages+keyFacts 340 api 1 high yes
Execution chain · AIPM · 1 round(s)
  1. Question: What related products, services, or pages are mentioned?
  2. Planner: Planner mapped question `related` on AIPM to minimal fields: relatedPages, sections, keyFacts.
  3. Slice: relatedPages+keyFacts — AIPM.relatedPages: WordPress plugin setup guide — https://aigeoradar.com/help/wordpress-plugin-setup...
  4. Actual input tokens: 340
  5. LLM answer: The related products, services, or pages mentioned are the WordPress plugin setup guide, AigeoRadar Connector WordPress GEO plugin, AI Page Manifest (AIPM) spec...
  6. Evidence: AIPM.relatedPages: WordPress plugin setup guide — https://aigeoradar.com/help/wordpress-plugin-setup AigeoRadar Connector WordPress GEO plug...
  7. Confidence: high · matched yes
Execution chain · HTML · 1 round(s)
  1. Question: What related products, services, or pages are mentioned?
  2. Planner: Planner mapped question `related` on HTML to minimal fields: lead_paragraphs, body_excerpt, prose_span.
  3. Slice: lead_paragraphs+body_excerpt+prose_span — HTML.lead_paragraphs: This guide shows WordPress owners how to publish llms.txt and related AI disco...
  4. Actual input tokens: 347
  5. LLM answer: The related products and services mentioned are AigeoRadar one-click deploy, a WordPress plugin, and a Shopify app for GEO discovery deploy.
  6. Evidence: HTML.lead_paragraphs: This guide shows WordPress owners how to publish llms.txt and related AI discovery files, and how AigeoRadar one-click...
  7. Confidence: high · matched yes
Execution chain · SCHEMA · 2 round(s)
  1. Question: What related products, services, or pages are mentioned?
  2. Planner: Planner mapped question `related` on SCHEMA to minimal fields: description, block, name.
  3. Slice: description+block+name+headline+publisher+@type+dateModified+datePublished — SCHEMA.description: Explains the WordPress GEO publishing workflow: which discovery assets to ship,...
  4. Actual input tokens: 566
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched no

Round 1: 252 in-tok · score 0 · miss

Round 2: 314 in-tok · score 0 · miss

HTML

sufficient

The related products and services mentioned are AigeoRadar one-click deploy, a WordPress plugin, and a Shopify app for GEO discovery deploy.

score 82 · 22,175 chars context · 347 in-tokens

HTML answered using 22175 chars of layer context (minimum fed this run).

Schema

insufficient

UNKNOWN.

score 0 · 15,629 chars context · 566 in-tokens

Schema did not answer from 15629 chars of context — additional or different layer context needed.

AIPM

sufficient

The related products, services, or pages mentioned are the WordPress plugin setup guide, AigeoRadar Connector WordPress GEO plugin, AI Page Manifest (AIPM) specification, Free AI Bot Checker, Shopify GEO app, and AigeoRadar llms-full.txt.

score 82 · 35,339 chars context · 340 in-tokens

AIPM answered using 35339 chars of layer context (minimum fed this run).

Q19 · Metadata · orientation

When was this page published or last updated?

Gold: 2026-07-31

Gold source: html.time|jsonld.dateModified · html_independent

Sufficient: HTML, Schema, AIPM

Measured retrieval cost (API tokens) · lowest cost AIPM · 134 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML time+lead_paragraphs+body_excerpt 339 api 1 high yes
Schema dateModified+datePublished+block 260 api 1 high yes
AIPM dateModified+datePublished+reviewDate 134 api 1 high yes
Execution chain · AIPM · 1 round(s)
  1. Question: When was this page published or last updated?
  2. Planner: Planner mapped question `freshness` on AIPM to minimal fields: dateModified, datePublished, reviewDate.
  3. Slice: dateModified+datePublished+reviewDate — AIPM.dateModified: 2026-07-31 AIPM.datePublished: 2026-07-31 AIPM.reviewDate: 2026-07-31
  4. Actual input tokens: 134
  5. LLM answer: The page was published and last updated on 2026-07-31.
  6. Evidence: AIPM.dateModified: 2026-07-31 AIPM.datePublished: 2026-07-31 AIPM.reviewDate: 2026-07-31
  7. Confidence: high · matched yes
Execution chain · HTML · 1 round(s)
  1. Question: When was this page published or last updated?
  2. Planner: Planner mapped question `freshness` on HTML to minimal fields: time, lead_paragraphs, body_excerpt.
  3. Slice: time+lead_paragraphs+body_excerpt — HTML.time: 2026-07-31 HTML.lead_paragraphs: This guide shows WordPress owners how to publish llms.t...
  4. Actual input tokens: 339
  5. LLM answer: The page was published or last updated on 2026-07-31.
  6. Evidence: HTML.time: 2026-07-31 HTML.lead_paragraphs: This guide shows WordPress owners how to publish llms.txt and related AI discovery files, and h...
  7. Confidence: high · matched yes
Execution chain · SCHEMA · 1 round(s)
  1. Question: When was this page published or last updated?
  2. Planner: Planner mapped question `freshness` on SCHEMA to minimal fields: dateModified, datePublished, block.
  3. Slice: dateModified+datePublished+block — SCHEMA.dateModified: 2026-07-31 SCHEMA.datePublished: 2026-07-31 SCHEMA.block: { "@context": "...
  4. Actual input tokens: 260
  5. LLM answer: The page was published and last updated on 2026-07-31.
  6. Evidence: SCHEMA.dateModified: 2026-07-31 SCHEMA.datePublished: 2026-07-31 SCHEMA.block: { "@context": "https://schema.org", "@graph": [...
  7. Confidence: high · matched yes

HTML

sufficient

The page was published or last updated on 2026-07-31.

score 88 · 22,175 chars context · 339 in-tokens

HTML answered using 22175 chars of layer context (minimum fed this run).

Schema

sufficient

The page was published and last updated on 2026-07-31.

score 88 · 15,629 chars context · 260 in-tokens

Schema answered using 15629 chars of layer context (minimum fed this run).

AIPM

sufficient

The page was published and last updated on 2026-07-31.

score 88 · 35,339 chars context · 134 in-tokens

AIPM answered using 35339 chars of layer context (minimum fed this run).

Q20 · Metadata · orientation

What kind of page is this (article, product, local service, company page, FAQ, other)?

Gold: blog

Gold source: html.heuristic|jsonld.@type · html_independent

Sufficient: AIPM · Needs more / other context: HTML, Schema

Measured retrieval cost (API tokens) · lowest cost AIPM · 124 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML meta_description+lead_paragraphs+title+h1+prose_span+body_excerpt+time 667 api 2 low no
Schema @type+name+headline+description+publisher+block+dateModified+datePublished 477 api 2 low no
AIPM contentIntent+pageType 124 api 1 high yes
Execution chain · AIPM · 1 round(s)
  1. Question: What kind of page is this (article, product, local service, company page, FAQ, other)?
  2. Planner: Planner mapped question `page_kind` on AIPM to minimal fields: contentIntent, pageType.
  3. Slice: contentIntent+pageType — AIPM.contentIntent: informational AIPM.pageType: blog
  4. Actual input tokens: 124
  5. LLM answer: blog
  6. Evidence: blog
  7. Confidence: high · matched yes
Execution chain · HTML · 2 round(s)
  1. Question: What kind of page is this (article, product, local service, company page, FAQ, other)?
  2. Planner: Planner mapped question `page_kind` on HTML to minimal fields: meta_description, lead_paragraphs.
  3. Slice: meta_description+lead_paragraphs+title+h1+prose_span+body_excerpt+time — HTML.meta_description: Publish llms.txt and llms-full.txt on WordPress with AigeoRadar. One-click GE...
  4. Actual input tokens: 667
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched no

Round 1: 221 in-tok · score 0 · miss

Round 2: 446 in-tok · score 0 · miss

Execution chain · SCHEMA · 2 round(s)
  1. Question: What kind of page is this (article, product, local service, company page, FAQ, other)?
  2. Planner: Planner mapped question `page_kind` on SCHEMA to minimal fields: @type.
  3. Slice: @type+name+headline+description+publisher+block+dateModified+datePublished — SCHEMA.@type: Organization SCHEMA.name: AigeoRadar SCHEMA.headline: WordPress GEO: llms.txt and AI...
  4. Actual input tokens: 477
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched no

Round 1: 114 in-tok · score 0 · miss

Round 2: 363 in-tok · score 0 · miss

HTML

insufficient

UNKNOWN.

score 0 · 22,175 chars context · 667 in-tokens

HTML did not answer from 22175 chars of context — additional or different layer context needed.

Schema

insufficient

UNKNOWN.

score 0 · 15,629 chars context · 477 in-tokens

Schema did not answer from 15629 chars of context — additional or different layer context needed.

AIPM

sufficient

blog

score 100 · 35,339 chars context · 124 in-tokens

AIPM answered using 35339 chars of layer context (minimum fed this run).

Methodology

  • AI Context Benchmark: Planner → Slice → LLM with measured API input tokens.
  • Reports describe sufficient layers, slice size, cost, and evidence — they do not declare a winning format.
  • Layers under test today: HTML, Schema.org, AIPM (extensible to RSS, Markdown, PDF, …).
  • Engine aipm_benchmark_v4 · Fri, Jul 31, 2026 2:42 PM · aipm_benchmark_score_v10

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