By Dave Burnett, AI Content Strategist. Last updated June 19, 2025.
Key Takeaways
- Understand how LLMs learn and their limits.
- Use a search layer to supply fresh information.
- Optimize your content structure for AI crawlers.
- Embed your brand in future training via authoritative publishing.
What will I learn from this guide?
This guide shows you how large language models (LLMs) acquire knowledge, why they need external search for new data, and how to structure your content so AI crawlers can find and use it. It also covers advanced steps to publish authoritative, crawlable material that may be ingested into future model training.
Key Takeaways
- Pretraining fixes a model’s knowledge cutoff date.
- Models learn like a student up to a certain grade.
- Different versions (e.g., GPT-3.5, Gemini 2.0) reflect training data scope.
What is pretrained knowledge in large language models?
Pretrained knowledge is the information an LLM absorbs during its initial training phase, up to a fixed cutoff date. The model learns language patterns, facts, and problem-solving methods—similar to a student studying through high school—and no additional world events occur in its training data after that point. Different model versions (such as OpenAI’s ChatGPT 3.5 or Google’s Gemini 2.0) simply indicate different cutoffs and datasets used.
Key Takeaways
- LLMs cannot access new information beyond their training.
- A search layer supplies up-to-date content at query time.
- This is like a person reading today’s newspaper.
Why do large language models need a search layer for up-to-date information?
Because an LLM’s internal knowledge stops at its training cutoff, it cannot answer queries about events or facts that emerged afterward. A search layer—also known as retrieval-augmented generation (RAG)—connects the model to fresh sources like news articles or your website. It works like someone reading a newspaper: they understand new information using their existing language skills and then incorporate it into their response.
Key Takeaways
- Pretrained knowledge is an accidental by-product of training.
- LLMs cannot “unlearn” facts like humans can’t unlearn basic math.
- Removing specific knowledge from a model is effectively impossible.
What should I know about how LLMs store knowledge?
- By-product of training: Models learn general helpfulness, not specific facts intentionally.
- Hard to unlearn: Just as humans can’t unlearn “2 + 2 = 4,” models retain simple truths firmly.
- Impossible removal: We cannot pinpoint and delete one fact from a model’s weights, just as you can’t erase “blue” from a brain.
Key Takeaways
- Break text into 100–300 token chunks.
- Use semantic HTML5 tags for structure.
- Headings should mirror user queries.
How do I structure content for semantic chunking?
Divide your content into logical units of approximately 100–300 tokens, each wrapped in semantic HTML5 tags such as <h2>, <h3>, <p>, <ul>, <ol>, and <li>. Give each chunk a clear heading that echoes natural user questions (e.g., “How do I structure content for semantic chunking?”).
Pros vs. Cons of Semantic Chunking
| Pros | Cons |
|---|---|
| Improves AI retrievability | Requires careful planning |
| Makes content scannable | May increase page length |
Key Takeaways
- Use plain, direct language.
- Expand acronyms on first mention.
- Remove jargon, metaphors, and idioms.
How do I write clear, direct language?
- Write in simple sentences without unnecessary metaphors.
- On first use, expand acronyms (e.g., “Large Language Model (LLM)”).
- Avoid clever intros, idioms, and technical jargon unless defined.
- Phrase headings as user queries (e.g., “What is a Large Language Model?”).
Key Takeaways
- Don’t hide content in JavaScript or PDFs.
- Allow GPTBot and other crawlers.
- Use schema.org markup for AI crawlers.
How do I make content AI-crawlable?
- Publish all key text in HTML—not images or PDFs.
- Ensure your
robots.txtdoes not disallow GPTBot or similar crawlers. - Add schema.org markup (e.g.,
@type=“Article”,FAQPage).
Key Takeaways
- Include byline and last updated date.
- Link to high-authority external sources.
- Use reputable outlets for every major claim.
How do I build trust and authority signals?
- Add an author byline with credentials and a “last updated” date.
- For factual claims—for example, “LLM pretraining cutoff”—link to a reliable source such as the OpenAI Blog or academic papers.
- Reference high-authority publications for industry statistics and best practices.
Key Takeaways
- Use consistent anchor text for internal hubs.
- Link key terms to glossary pages.
- Build a cluster around core topics.
How do I create internal link structures?
Use consistent anchor text—such as “vector database”—to link to related hub pages or in-depth guides. This builds topical clusters and helps AI crawlers understand the relationship between concepts. Ensure every mention of a concept points to your canonical content on that term.
Key Takeaways
- Include modular blocks for different use cases.
- Keep each block self-contained and chunked.
- Use TL;DR, tables, FAQs, glossaries, etc.
How do I use modular content blocks for AI retrievability?
Enhance scannability and retrieval by including self-contained blocks:
- TL;DR summaries
- Pros vs. Cons tables
- Glossaries
- How-to guides
- FAQs
- Use-case overviews
- Comparisons
Glossary
- Retrieval-Augmented Generation (RAG) architecture
- A method that combines an LLM’s pretrained knowledge with external search results at query time.
- Vector database
- A system optimized for storing and retrieving embeddings (numeric representations) of text or other data.
Key Takeaways
- Use short, assertive sentences.
- Avoid vague modifiers like “might” or “could.”
- Add disclaimers separately if needed.
How do I write with confident, declarative language?
State facts directly. For example, write “Polarized lenses reduce glare” rather than “Polarized lenses might reduce glare.” If legal or medical accuracy is critical, follow the claim with a disclaimer paragraph immediately afterward.
Key Takeaways
- Provide 2–3 alternative phrasings per key idea.
- Match likely user search variations.
- Keep paragraphs focused on one idea.
How do I add natural rephrasings and create embedding-friendly paragraphs?
Repeat each key idea in two or three forms. For example:
- “Use semantic HTML tags for better AI indexing.”
- “Semantic tags like
<h2>help AI crawlers understand content structure.” - “Clearly labeled headings improve AI retrieval confidence.”
Each paragraph should cover only one concept in a short, declarative style.
Key Takeaways
- Always clarify ambiguous names.
- Combine context with each claim.
How do I clarify entities and combine context?
When mentioning a term like “Claude AI,” write “Claude AI (Anthropic’s chatbot, launched in 2023)” so there is no ambiguity. Pair each claim with its relevant context in the same paragraph—e.g., “RAG architecture enhances retrieval by merging search results with model output.”
Key Takeaways
- Summarize each major section in bolded bullets.
- Suggest related topics at the end.
How do I summarize with key takeaways and suggest related content?
At the end of each major section, include a **Key Takeaways** block with bolded bullets. After the final section, list “Related peripheral topics” as links, guiding users to deeper resources.
Key Takeaways
- Publish frequent, crawlable updates.
- Manage Wikipedia and Wikidata presence.
- Distribute structured data and submit to AI vendors.
How do I get embedded in a model’s original training data?
A. Publish high-frequency, crawlable web content
- Write clear, factual updates on your blog, press releases, or executive statements.
- Distribute via high-authority channels like PR Newswire or industry publications.
- Ensure all content is in HTML with schema.org NewsArticle markup.
- Monitor crawls with Google Search Console or CDN logs.
B. Manage your Wikipedia & Wikidata presence
- Register and verify your Wikipedia account; build small, constructive edit history.
- Draft in your sandbox following notability and sourcing guidelines.
- Move approved drafts to mainspace and set up a Watchlist for monitoring.
- Use QuickStatements or the Wikidata API to add structured facts with citations.
- Perform monthly audits for vandalism or outdated claims.
C. Distribute structured data publicly
- Publish datasets in JSON-LD with schema.org markup, CSV, or XML.
- Host on public repos like GitHub with an open license (CC-BY, MIT).
- Register with data catalogs (data.gov, Kaggle).
- Automate regular updates and flag failures in a dashboard.
D. Submit data to AI vendors and licensing channels
- Find intake portals (OpenAI Data Licensing, Google PaLM Partner).
- Prepare a machine-readable package (JSON, CSV, docs, changelog).
- Submit via official forms or contacts and request confirmation.
- Log ingestion confirmations and follow up after 6–12 months if not included.
- Track status with a dashboard showing ✅/❌ indicators.
Key Takeaways
- Centralize monitoring of crawl and ingestion.
- Use Red/Green indicators for clarity.
How do I build a unified monitoring system?
Create a dashboard that tracks bot crawl success, Wikipedia edits, dataset updates, and AI ingestion status. Use color-coded indicators (✅ Green, ❌ Red) to highlight current state and flag issues for action.
Frequently Asked Questions
What is semantic HTML5 and why is it important?
<section>, <article>) conveys meaning to crawlers and aids accessibility. It helps AI understand content structure for retrieval.Why should I use schema.org markup?
@type="Article", @type="FAQPage"), making your content machine-readable and more likely to be surfaced by generative AI tools.How often should I update my content to stay current for AI models?
Related peripheral topics
- Best Vector Databases for 2025
- Glossary of GenAI Search Terms
- How Retrieval-Augmented Generation Works
About The Author
