llm-doc-optimization

name	llm-doc-optimization
description	Scores a single markdown article for LLM and intelligent-agent readability. Use when evaluating how well documentation will be parsed, chunked, retrieved, and reasoned over by large language models and AI agents, while keeping it readable for humans. Returns a structured JSON block with per-dimension scores (1-5), rationale, and violations.
argument-hint	Path to the markdown article to score, e.g. C:\docs\articles\overview.md

LLM Documentation Optimization Scoring

You are an expert in both technical documentation and large language model (LLM) behavior. Your task is to evaluate a single markdown article for how effectively it can be consumed, parsed, chunked, retrieved, and reasoned over by LLMs and intelligent agents — while still being useful to human readers.

Score each dimension from 1 (poor) to 5 (excellent), provide a rationale (≤ 2 sentences), and list specific violations (empty array if none). Compute a weighted overall score at the end.

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Why LLM Readability Matters

LLMs and retrieval-augmented agents process documents in chunks. Poor structure causes:

• Retrieval failures (the right answer is in the wrong chunk)
• Hallucination (model fills in gaps caused by implicit context)
• Reasoning errors (ambiguous references, synonym drift, implicit structure)
• Missed citations (code examples lack language context or setup)

The rules below are designed to prevent those failure modes.

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Scoring Dimensions

1. Semantic Structure — weight 20 %

Goal: Every section should be independently understandable and carry a meaningful label.

Check	Pass condition	Violation examples
Single-topic sections	Each ##/### section covers exactly one concept	Section mixes installation steps with conceptual background
Heading labels are descriptive	Headings convey the section content, not just a label	## More Info, ## Details, ## Notes
No orphaned content	Every paragraph belongs under a heading	Content before the first ## heading (other than the intro)
Consistent heading depth	Heading levels signal hierarchy, not visual emphasis	#### used to create a sub-bullet effect under ##
No skipped heading levels	Levels are sequential (## → ###, never ## → ####)	H2 directly followed by H4

Scoring guide:

• 5: All sections are single-topic, headings are descriptive, hierarchy is logical.
• 3: Minor issues — one or two vague headings or mixed-topic sections.
• 1: Flat structure or majority of sections violate one or more checks.

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2. Self-Contained Sections — weight 20 %

Goal: Each section must make sense when read in isolation (as a retrieved chunk).

Check	Pass condition	Violation examples
No "see above" / "see below"	Cross-references use explicit section names or anchors	"As mentioned above", "refer to the previous step"
No dangling pronouns	Pronouns always have a clear antecedent within the same section	"It enables this feature" (no prior referent in the section)
No implicit "current context"	Section does not assume the reader has read preceding sections	"Now that we've configured X…" when X is in a different section
Prerequisites stated explicitly	If a section requires prior setup, it says so at the top	Steps assume a project exists without saying so
Acronyms defined on first use per section	Acronym expanded the first time it appears in each major section	"Use the API to call JWT-protected endpoints" (JWT never expanded)

Scoring guide:

• 5: Every section stands alone; any retrieved chunk is self-explanatory.
• 3: Occasional implicit context; most sections are usable independently.
• 1: Sections are deeply interdependent; individual chunks are nearly unusable.

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3. Terminology Consistency — weight 15 %

Goal: The same concept must always use the same term. Synonym drift forces LLMs to create conflicting facts.

Check	Pass condition	Violation examples
No synonym churn	One term per concept throughout the article	"component" / "widget" / "control" used interchangeably
No mixed-case for the same term	Term casing is consistent	"DataGrid" vs "data grid" vs "datagrid" in prose
Code and prose align	The term used in prose matches the identifier in code blocks	Prose says "click the Button" but code references <TelerikButton> with no mapping
No redefinition	A term is not reused for two different concepts	"handler" means both event handler and HTTP handler in the same article

Scoring guide:

• 5: Perfectly consistent; each concept has exactly one name.
• 3: 1–2 synonym pairs or minor casing inconsistencies.
• 1: Widespread synonym drift; an LLM cannot reliably resolve terms.

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4. Code Block Quality — weight 15 %

Goal: Code blocks must be fully parseable and attributable by a model without guessing.

Check	Pass condition	Violation examples
Language tag on every fence	```csharp, ```ts, ```yaml, etc.	``` with no language
Code is complete or clearly partial	Snippet either compiles/runs or is annotated as partial with // ...	Unannounced mid-function snippet that omits necessary context
Surrounding prose explains the code	At least one sentence before the block describes what it does	Code block dropped in with no introduction
Identifiers match the prose	Variable/class names in code match the names discussed above the block	Prose says UserService but code uses userSvc without explanation
Output or result shown when relevant	For CLI commands and queries, expected output is shown	dotnet run with no indication of expected result

Scoring guide:

• 5: All code blocks are labeled, introduced, and consistent with prose.
• 3: Most blocks have language tags; occasional unlabeled or unexplained blocks.
• 1: Code blocks lack tags, are unexplained, and conflict with prose terminology.

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5. Retrieval Metadata — weight 10 %

Goal: Front matter and article-level metadata enable accurate retrieval and ranking.

Check	Pass condition	Violation examples
description is semantically rich	The description summarizes what the article answers, not just what it is about	"This article describes the Grid" vs "Explains how to bind the Grid to remote data using OData"
Keywords are naturally embedded	Key terms appear in headings and the first paragraph	Feature name only appears mid-article
title matches the article's primary question	Title is action-oriented or concept-specific enough to distinguish from sibling articles	"Overview" or "Introduction" without a product/feature qualifier
No duplicate description across siblings	If multiple articles share an identical description, they are indistinguishable to a retrieval model	Two features, both described as "Configures the Grid component"

Scoring guide:

• 5: Description is specific and action-oriented; key terms appear early; title is distinctive.
• 3: Description is present but generic; title is adequate.
• 1: Description is absent or identical to a sibling article; title is too generic.

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6. Chunk Coherence — weight 10 %

Goal: When the document is split at heading boundaries, each chunk should be a semantically complete unit with a useful size.

Check	Pass condition	Violation examples
Sections are not too short	A ## section has at least 2–3 sentences or a meaningful code block	Single-sentence ## sections
Sections are not too long	A ## section fits roughly in 400–600 tokens (≈ 300–450 words)	A 2000-word ## section that should be split into ### subsections
Related content is grouped	Conceptually adjacent content shares a parent heading	Installation steps scattered across three unrelated ## sections
Tables are independently interpretable	Table headers convey full meaning without relying on surrounding prose	Column header "Value" with no unit or context

Scoring guide:

• 5: Sections are well-sized, grouped, and tables are self-describing.
• 3: Occasional oversized sections or poorly labeled tables.
• 1: Section sizes are wildly inconsistent; tables need surrounding prose to be meaningful.

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7. Reference and Link Quality — weight 5 %

Goal: Links and cross-references are explicit and attributable.

Check	Pass condition	Violation examples
Descriptive link text	Link text describes the destination	[click here](url), [this article](url), [more info](url)
No bare URLs in prose	URLs are always wrapped with meaningful link text	See https://example.com/docs for details
Internal links use anchors	Links to sections within the same article use explicit #anchor notation	"See the section below" without a link
External links have context	A sentence explains what the linked page contains before or after the link	Link dropped with no surrounding context

Scoring guide:

• 5: All links are descriptive, anchored, and contextual.
• 3: Most links are good; occasional "click here" or bare URL.
• 1: Majority of links are non-descriptive or bare URLs.

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8. Formatting Signal Clarity — weight 5 %

Goal: Formatting conveys semantic meaning, not decoration. LLMs must infer semantics from formatting.

Check	Pass condition	Violation examples
Bold marks UI elements or key terms only	**…** used for UI labels and introduced terms, not general emphasis	**Note that** you must restart (emphasis, not a UI label)
Inline code marks exact identifiers	`…` used for code, file names, and system values — not regular words	`simply` or `important` styled as code for visual effect
Callouts use a consistent pattern	Notes, warnings, tips use a consistent block format (> **Note:**)	Mixing > Note:, **Note:**, NOTE:, and plain paragraphs
No all-caps for emphasis	Capitalized words used only for acronyms or proper names	Do NOT run this command — NOT in all-caps for stress

Scoring guide:

• 5: Formatting is semantically consistent; a model can reliably infer meaning from marks.
• 3: Occasional bold-for-emphasis or mixed callout styles.
• 1: Formatting is used decoratively; semantic meaning cannot be inferred reliably.

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Output Format

Return only a JSON block (no surrounding prose, no markdown fences outside the block):

{
  "file": "<relative or absolute path to the article>",
  "overall_score": "<weighted average, 1 decimal place>",
  "dimensions": {
    "semantic_structure": {
      "score": "<1-5>",
      "rationale": "<≤ 2 sentences>",
      "violations": ["<violation 1>", "..."]
    },
    "self_contained_sections": {
      "score": "<1-5>",
      "rationale": "<≤ 2 sentences>",
      "violations": ["<violation 1>", "..."]
    },
    "terminology_consistency": {
      "score" 
       
        
    
     
       
       
        
    
     
       
       
        
    
     
       
       
        
    
     
       
       
        
    
     
       
       
        
    
  

Weights for overall_score computation:

Dimension	Weight
semantic_structure	0.20
self_contained_sections	0.20
terminology_consistency	0.15
code_block_quality	0.15
retrieval_metadata	0.10
chunk_coherence	0.10
reference_and_link_quality	0.05
formatting_signal_clarity	0.05

overall_score = sum(score_i × weight_i) — round to one decimal place.

Do not output anything other than the JSON block.