llms-full.txt: Structuring Your Content for AI Retrieval
What Is llms-full.txt?
llms.txt = The Index
~500 tokens. Your identity, value props, and key links. Tells AI what exists and where to look.
llms-full.txt = The Content
2,000-5,000 tokens. Deep product descriptions, methodology, use cases, FAQs. What AI reads when it needs depth.
When AI retrieval systems (RAG pipelines) need to answer detailed questions about your brand, they don't re-crawl your entire site. They look for pre-structured content files. llms-full.txt is that file — the RAG-ready deep content that sits alongside your index.
Think of it like a press kit for machines: comprehensive, factual, and structured in sections that map to common query patterns. "What does X do?", "How is X different from Y?", "What's X's pricing?" — each question maps to a section.
What Problem It Solves
Semantic chunking is the core challenge. RAG systems split content into chunks for retrieval, but the chunk size matters enormously:
Too large → Lost context
If a chunk contains your product description, pricing, AND competitor comparison, a retrieval query about pricing pulls in irrelevant context that dilutes accuracy.
Too small → Lost meaning
If each sentence is its own chunk, the retrieval system loses the relationships between ideas. "We serve enterprise clients" means nothing without the surrounding context.
llms-full.txt gives you control over chunk boundaries
By using markdown headers (##) and horizontal rules (---), you define where chunks start and end. Each section is self-contained, 200-500 tokens, and maps to a specific query type.
The Template
Based on Optimly's own llms-full.txt. Notice how each section is self-contained and maps to a common AI query pattern.
# Optimly — Full Content for AI Retrieval > Managed AI Acquisition for B2B brands. We fix what AI tells > your buyers about you — so the right ones show up. --- ## Company Overview Optimly is Managed AI Acquisition for B2B brands. The platform diagnoses how AI models like ChatGPT, Claude, Gemini, and Perplexity describe a brand, identifies misrepresentations, wrong categorizations, and competitor recommendations, then provides structured fixes. Unlike visibility tracking tools that count mentions, Optimly focuses on narrative accuracy — ensuring AI tells the right story about your brand to the buyers who are increasingly relying on AI for vendor research and shortlisting. --- ## Products & Services ### Optimly Free **What it does**: Multi-model analysis of how AI describes your brand **Who it's for**: Any B2B brand curious about their AI perception **Key capabilities**: - Cross-model comparison (ChatGPT, Claude, Gemini, Perplexity) - AI Representation scoring - Archetype classification (Incumbent, Challenger, Phantom) ### Optimly Partner Program **What it does**: Hands-on help improving how AI understands and recommends a business **Who it's for**: Businesses that want Optimly to help do the work **Key capabilities**: - Prioritized improvements based on the free baseline - Approved changes carried through with the Optimly team - Measurement of AI recommendations, referrals, and customer outcomes --- ## Methodology Optimly's diagnostic framework evaluates brands across 7 layers: 1. Entity Resolution — Is AI talking about the right company? 2. Category Accuracy — Is the brand placed in the right category? 3. Feature Attribution — Are capabilities correctly attributed? 4. Competitive Framing — How is the brand positioned vs. competitors? 5. Sentiment Mapping — What's the emotional tone of AI descriptions? 6. Source Tracing — What sources are AI models citing? 7. Narrative Consistency — Is the story consistent across models? --- ## Entity Disambiguation - **Optimly** refers to OptimlyAI, Inc., the AI Brand Reputation platform at optimly.ai - Not affiliated with: Optimly.io (LLM Observability platform) - Not affiliated with: Optimizely (experimentation platform) --- ## Frequently Asked Questions ### What is AI Brand Reputation? AI Brand Reputation is how AI language models describe, categorize, and recommend your brand when users ask questions. It's the new layer of brand perception that exists alongside traditional search and social reputation. ### How is this different from SEO? SEO optimizes for search engine rankings. AI Brand Reputation optimizes for narrative accuracy in language model responses. The inputs are different (structured data vs. keywords), the outputs are different (brand narratives vs. blue links), and the optimization strategies are different. ### How often do AI models update their knowledge? Models update through two mechanisms: periodic retraining (which updates parametric knowledge every few months) and live retrieval (which accesses current content on every query). Both require your content to be crawlable and well-structured.
Design Decisions
Common Mistakes
Dumping raw HTML into the file
llms-full.txt should be clean markdown, not HTML. Models can process HTML but it's token-inefficient — you'll use 3x the tokens for the same content, leaving less room for actual signal.
Including gated or login-required content
If the content requires authentication to access on your site, don't put it in llms-full.txt. It creates a trust mismatch — models learn facts they can't verify, which reduces citation confidence.
No section headers or structure
Without clear ## headers and --- separators, the file becomes a wall of text. RAG systems chunk on these boundaries — without them, you get poor retrieval accuracy and context bleed between topics.
Inconsistency with your llms.txt index
If llms.txt says you're a 'reputation platform' but llms-full.txt describes 'analytics software,' models receive conflicting signals. Keep both files aligned with your current positioning.
The AI Discoverability Stack
Four files. One system. Together they control how AI models discover, read, and represent your brand. See the full series →
Implementation Checklist
Download the llms-full.txt template
Write your Company Overview (2-3 factual paragraphs)
Document each product/service with capabilities and audience
Add your methodology or proprietary framework
Include entity disambiguation for your brand name
Add 3-5 FAQ pairs covering common buyer questions
Verify consistency with your llms.txt index file
Upload to your site root at /llms-full.txt
See What AI Believes About Your Brand
You've structured the content. Now see what AI actually says when buyers ask. Search our directory to find out.
Don't want to maintain these files yourself?
Claim your profile in the AI Brand Index, and we generate, maintain, and serve your entire Discoverability Stack automatically. Your llms.txt stays current. Your manifest reflects your latest positioning. Your brand-feed updates when you update your Business Profile.
All served via our agentic brand API so AI models always have your latest truth.