The definitive content guide
An LLM is like a person that has gone to school.
It learns everything up to a certain point, say for example being a junior in high school. It learns math, science, and history up to that point.
And that’s all it knows.
It doesn’t know senior level math, or history, or science. It simply hasn’t learned it.
That knowledge is all gathered together and becomes the basis for ChatGPT 3.5, or Claude Sonnet, or Gemini 2.5. This is why there are different models and model numbers. They clarify the knowledge the models have been trained on.
Once the initial knowledge is ‘pretrained’, there is some additional training so any responses are helpful and beneficial. This makes sure the models are safe to interact with, however this training is all about accessing what the model knows.
To address the fact that the model doesn’t have up-to-date information, and it can’t access information it hasn’t learned, there is a second layer that’s added to most models now; a search layer.
Think of it like a person reading something from a newspaper. They have the fundamentals of knowing how to read, and understand what they are reading, and yet the information is new and relevant, so they understand it and can repeat it back now that they know it.
There are a couple more important things you should know about the pretrained knowledge models have.
- The first is the knowledge they have is almost a bi-product of their training. The training was done so the models could become helpful and useful, not necessarily to teach them specific things.
- The second thing is with AI models it’s very hard to ‘unlearn’ what it’s learned, just like it would be difficult for you to unlearn that 2 + 2 = 4.
- The third thing is it’s hard to remove knowledge from a model. Using people as an example, it’s hard for people to know exactly where a specific word is stored in the brain. If you wanted to forget the word ‘blue’, you could pinpoint the language area of the brain, but not exactly where that word is stored to remove it. It’s the same with AI models. So once their knowledge is set, it’s basically impossible to change because we don’t know where ‘blue’ is stored in their knowledge set either.
So what can you do? You can make sure the knowledge the models have is accurate, up to date, and relevant for you and your brand.
How do you do that?
The first thing you can do is make sure you are showing up in today’s ‘newspaper’ or more accurately, making sure your current information is structured properly for model consumption.
How to Rank in AI-Powered Search
The third thing is it’s hard to remove knowledge from a model. Using people as an example, it’s hard for people to know exactly where a specific word is stored in the brain. If you wanted to forget the word ‘blue’, you could pinpoint the language area of the brain, but not exactly where that word is stored to remove it. It’s the same with AI models. So once their knowledge is set, it’s basically impossible to change because we don’t know where ‘blue’ is stored in their knowledge set either.
1. Structure for Semantic Chunking
- Break the content into logical “chunks” of 100–300 tokens (75-225 words).
- Use semantic HTML5 tags such as <h2>, <h3>, <p>, <ul>, <ol>, and <li>.
- Ensure each chunk has a clear heading that mirrors a natural user query.
2. Use Clear, Direct Language
- Rewrite all headings and body text in plain, jargon-free language.
- Expand all acronyms on first mention.
- Remove clever intros, idioms, and unnecessary metaphors.
- Phrase FAQs and section headings exactly as a user might search for them.
3. Make Content AI-Crawlable
- Ensure that key content is not hidden in JavaScript, images, or PDF files.
- Allow AI crawlers like GPTBot in your robots.txt file.
- Use schema.org markup where relevant (e.g., Article, FAQPage).
4. Build Trust and Authority
- Include an author byline with credentials and a last updated date.
- Link to at least one reputable external source for every major claim you make.
5. Create Internal Link Structures
- Use consistent anchor text to link to related internal content.
- Mentioned terms (e.g., “RAG architecture,” “vector database”) should link to glossary or in-depth topic pages.
Final Tip: Build a Unified Monitoring System
- Enhance scannability with self-contained blocks such as:
- TL;DR summaries
- Pros vs. Cons tables
- Glossaries
- How-to guides
- FAQs
- Use case overviews
- Comparisons
- Make sure each block follows the 100–300 token (75 to 225 words) chunk guideline.
Final Tip: Build a Unified Monitoring System
- Avoid vague language like “might” or “some believe.”
- Present factual claims assertively.
- If necessary, add disclaimers after the claim (e.g., for legal or medical topics).
8. Add Natural Rephrasings
- Repeat key ideas using 2–3 alternative phrasings throughout the article.
- Match these variations to how real users might phrase the same query.
9. Keep Paragraphs Embedding-Friendly
- Focus on one idea per paragraph.
- Use short, declarative sentences.
- Avoid multi-topic or overly long blocks of text.
10. Add Clarifying Context for Entities
- When mentioning people, brands, or products, always clarify with extra context:
- E.g., “Claude AI (Anthropic’s chatbot, launched in 2023)” instead of just “Claude.”
11. Combine Claims with Context
- Place each key fact alongside its context in the same paragraph.
- Example: “Polarized lenses reduce glare from water and glass — a key benefit for anglers and drivers.”
12. Summarize with Key Takeaways
- At the top of each major section, include a Key Takeaways block.
- Use bolded bullet points to make them easy to scan.
13. Suggest Related Content
- Finish with links to peripheral but relevant topics, such as:
- Best Vector Databases for 2025”
- “Glossary of GenAI Search Terms”
- “How Retrieval-Augmented Generation Works”
Final Output Requirements
Your restructured content should:
- Use valid HTML5 with semantic tags.
- Contain headings (<h2>, <h3>) that reflect user search queries.
- Be chunked into 100–300 token sections.
- Include internal links, external citations, schema markup, and modular components.
By following these steps, your content will be structured for both human readability and AI retrievability — giving it the best chance to rank, be embedded, and be surfaced in GenAI-powered tools.
How to get found in the next chatbot model:
If you want to be found in the model’s original knowledge, that is a longer, more difficult process.
The steps below outline how to publish authoritative, crawlable content that AI model developers and data pipelines can detect, index, and use in training.
A. Publish High-Frequency, Crawlable Web Content
- Create Authoritative Content
- Publish clear, factual updates on your company blog, press releases, or executive statements.
- Focus on events, product launches, partnerships, and milestones that have verifiable, secondary sources.
- Distribute via High-Traffic Outlets
- Use press release services like PR Newswire or Business Wire.
- Pitch guest posts or news articles to high-authority industry publications.
- Ensure Content Is AI-Crawlable
- Publish in HTML format — not PDFs or gated pages.
- Add schema.org markup (e.g., NewsArticle, Organization) to surface key facts for crawlers.
- Monitor Crawl Activity
- Use Google Search Console or CDN logs to confirm visits from bots like Common Crawl, GPTBot, or Bingbot.
- Set up a dashboard to track crawl activity using color-coded status indicators (e.g., ✅ Green = crawled; ❌ Red = not yet crawled).
B. Manage Your Wikipedia & Wikidata Presence
- Create and Prepare Your Wikipedia Account
- Register and verify your email.
- Build edit history with small, constructive edits to reduce the chance of reverts.
- Draft in Your Sandbox
- Follow Wikipedia’s notability and sourcing guidelines.
- Use secondary, verifiable sources to back your edits or new page draft.
- Publish and Monitor Articles
- Move approved drafts to the mainspace.
- Set up a Watchlist or use monitoring tools to track future changes.
- Maintain Wikidata Accuracy
- Use QuickStatements or the Wikidata API to add structured facts (e.g., headquarters location, CEO, founding year).
- Reference every property with a source.
- Perform Regular Audits
- Check monthly for vandalism, outdated claims, or new attributes you should add.
- Use a dashboard to track Wikidata changes, verify red/green update status, and cross-check for consistency with your original data.
C. Distribute Structured Data Publicly
- Choose a Schema Format
- Use industry standards like:
- JSON-LD with schema.org markup
- CSV or XML if publishing to open data portals
- Use industry standards like:
- Publish to a Public Repository
- Upload your data to platforms like GitHub or GitLab.
- Include a clear open license such as CC-BY or MIT.
- Submit to Data Indexes
- Register your datasets with open catalogs such as:
- data.gov
- Kaggle Datasets
- Register your datasets with open catalogs such as:
- Automate Your Updates
- Integrate your data pipeline to publish updated files on a regular schedule (e.g., daily, weekly).
- Set up a dashboard to monitor data distribution, update status, and flag any failures (Red/Green logic).
D. Submit Data to AI Vendors and Licensing Channels
- Identify Submission Channels
- Explore data intake portals from major AI companies, such as:
- OpenAI’s Data Licensing Program
- Google’s PaLM Partner Portal
- Anthropic’s research feedback form
- Explore data intake portals from major AI companies, such as:
- Prepare a Machine-Readable Data Package
- Include:
- Structured facts (e.g., JSON, CSV)
- Documentation
- Changelogs
- Include:
- Submit and Follow Up
- Use official forms or contact representatives directly.
- Ask for confirmation that your data is included in the next training cycle.
- Document All Confirmations
- Log any ingestion confirmations or email responses.
- If updates don’t appear after 6–12 months, follow up with context and proof of submission.
- Monitor Ingestion Status
- Maintain a dashboard to track whether your data was accepted, when it was submitted, and its current status.
Final Tip: Build a Unified Monitoring System
For all the above strategies, invest in a centralized dashboard to:
- Track crawl success and data ingestion
- Audit Wikipedia/Wikidata changes
- Monitor dataset freshness
- Visualize status with Red/Green indicators for transparency and actionability
This systematic approach helps ensure your organization’s data is not only published, but also seen, trusted, and embedded in the next wave of AI model training sets.