Learn how to future-proof your content by turning it into an AI-ready knowledge base. This guide walks you through using embeddings and vector stores to boost visibility in tools like ChatGPT and Perplexity.
- Start With High-Performing Content
- Break Content Into 100 – 300 Word Semantic Chunks
- Turn Chunks Into Embeddings Using OpenAI or Cohere
- Store Embeddings in a Vector DatabaseÂ
- Plug Your Embeddings Into a RAG System
- Optimise For AI Visibility and LLMO Performance
Let’s be honest. These days, simply doing traditional SEO isn’t enough.
People aren’t always heading to Google and clicking around like they used to. Now they’re asking tools like ChatGPT or Perplexity to get straight answers without the extra steps.
So, your content has a new role. It still needs to be solid, but now it also has to make sense to AI tools. They need to understand it, pull from it, and use it when someone asks a question.
One of the best ways to make that possible? Take the content that already works, break it into smaller, focused bits, and turn those into something AI can read and work with. That process involves creating embeddings and storing them in a specialized database that enables AI to retrieve what it needs in real-time.
Here’s a no-nonsense guide: just real steps, no buzzwords or fluff.
Recommended For You:
- Why Is My Web Traffic Down But Ranking Is Stable?
- LLMO vs SEO: How To Optimise When AI Disrupts Your Traffic
- AI Search Visibility Strategies for 2025: 10 Experts Share How
- LLMO Audit Checklist [2025]: Diagnose Why LLMs Are Skipping Your SiteÂ
Why Embedding Your Content is the New Front Page of Google
More and more people are getting their answers directly from AI interfaces instead of clicking on search results, reshaping how we find information.
According to a 2024 study by Search Engine Land, about 58% of Google searches now end without a click, meaning people get what they need directly from AI-generated answers in the search results.
That shift is a clear signal. If you want your content to be visible, it needs to be structured in a way that AI tools can understand, retrieve, and reuse.
Your content needs to be findable through semantic similarity. That’s where embeddings and vector databases come in. They help AI understand what your content means, not just what words it uses.
Step 1: Start with High-Performing Content
Before you create anything new, look at what’s already working. The best candidates for embedding are the pages that already rank well or answer specific questions.
Examples include:
- Blog posts that rank high in search or get lots of traffic
- Help docs or knowledge base articles with long time-on-page
- Evergreen FAQ content
- How-to guides or definition pages
Pick content that clearly answers a question, explains a topic, or walks someone through a process.
How to Implement
- Open your analytics tool like GA4 or Search Console
- Export a list of top pages by organic traffic and engagement
- Highlight evergreen, FAQ-style, or highly structured content
- Pick the top 10 to 20 to start embedding
- Add them to a shared content tracker or spreadsheet
Step 2: Break Content into 100 to 300 Word Semantic Chunks
LLMs like GPT-4 and Claude don’t index full pages. They retrieve information in small, meaningful segments. That’s why you need to split up your content into what we call “chunks.”
These chunks should:
- Be between 100- 300 words
- Focus on a single idea or answer
- Be self-contained and easy to understand
Let’s say you have a blog titled “How AI Is Changing Supply Chains.” That could be split into:
- An intro on why supply chains are evolving
- A section on forecasting with AI
- A section on real-time analytics
- A section on predictive maintenance
- A wrap-up with ROI examples
Each one becomes its own retrievable piece.
How to Implement
- Paste your article into a doc editor
- Add headings (H2 or H3) to outline the structure
- Break up paragraphs by idea, not just formatting
- Copy each chunk into a spreadsheet or CMS field
- Tag with metadata like title, source URL, and topic
Step 3: Turn Chunks into Embeddings Using OpenAI or Cohere
Now, take each chunk and turn it into an embedding. This is a numerical vector that captures the meaning behind the words. This is how AI systems remember and search for your content.
Popular tools for this step:
- OpenAI’s text-embedding-3-large
- Cohere’s multilingual embedding model
- HuggingFace models via sentence-transformers
You’ll get a long list of numbers (a vector) for each chunk. These vectors allow retrieval based on meaning rather than matching words.
How to Implement
- Choose your embedding provider, such as OpenAI, Cohere, or HuggingFace
- Write a short script to send your chunks to the API
- Store the resulting vectors in JSON or CSV format
- Attach metadata like title, URL, and section
- Test a few sample queries to see what gets retrieved
Step 4: Store Embeddings in a Vector Database
Embedding vectors is great, but to use them effectively, you need a place to store and search them. That’s where vector databases come in.
Top choices include:
- Weaviate, which is open-source and schema-friendly
- Pinecone, known for being fast and scalable
- Supabase with pgvector, a good option if you already use Postgres
These tools let you run searches based on similarity to a question or phrase.
How to Implement
- Pick a vector DB based on your tech stack
- Create a project and get your API credentials
- Write a script or use the SDK to upload embeddings
- Include metadata for traceability and filtering
- Run some queries to make sure everything works
Step 5: Plug Your Embeddings into a RAG System
Retrieval-Augmented Generation, or RAG, is the engine behind many smart assistants and chatbots. It pulls in the most relevant content chunks and gives them to the AI before generating a response.
That means your content can show up in real-time conversations and tools.
Popular tools to make this happen:
- LangChain or LlamaIndex to organise everything
- OpenAI or Anthropic models to handle the replies
- Your vector DB to handle search and retrieval
How to Implement
- Choose a framework like LangChain or LlamaIndex
- Load your vector DB into the app
- Set up a retrieval chain to grab three to five chunks per query
- Add a system prompt that sets the tone and limits
- Deploy as a chatbot, API, or internal search tool
Step 6: Optimise for AI Visibility and LLMO Performance
Embedding helps, but there’s more you can do to increase visibility in AI responses. These tips will help your content stand out.
Add Schema Markup
- Use the FAQPage, TechArticle, or DefinedTerm schema
- Add JSON-LD snippets to FAQs, guides, and glossaries
Internally Link Your Chunks
- Link important terms to their definitions
- Build topic hubs that bring related content together
- Use clean anchor links and structure
Track How AI Uses Your Content
- Use Langfuse or LLMonitor to check AI prompts and hits
- Set filters in GA4 to track traffic from tools like ChatGPT
- Run sample searches in AI tools and see if you show up
How to Implement
- Check your site in Google’s structured data tester
- Add schema markup to blog and FAQ templates
- Connect related chunks using internal links
- Filter for AI-origin traffic in analytics
- Create a recurring log of your AI visibility
Bonus: Distribute and Scale for Maximum Reach
You don’t have to keep your best content confined to your own site. Get it out where AI can find it.
Post Chunks on External Platforms
- Share to Reddit in the right subreddits
- Answer questions on Quora, StackOverflow, or Dev.to
- Republish summaries on Medium or LinkedIn
Turn Chunks into Multimedia
- Make how-to videos for YouTube Shorts
- Convert lists into infographics or slides
- Turn definitions into tweet threads
How to Implement
- Choose five strong pieces of content to repurpose
- Break them into Q&A-style micro posts
- Create visuals, short clips, or audio segments
- Post on forums, social platforms, and blogs
- Monitor for AI citations and references
Final Thoughts: Make Your Content Work Like an AI Memory
When you structure your content for AI, you give it a second life. Instead of sitting on a blog post, it can become part of an intelligent system that retrieves and shares your ideas in real time.
This doesn’t require a huge team or complex coding. With a smart approach to chunking, embedding, and distribution, your content becomes a go-to resource for AI-driven platforms.
Need help getting started? I can put together a custom plan with embedding tools and a vector database setup based on what you already have.
Or, you might want to take our free AI Visibility Scorecard to learn if your brand is AI-ready and gain actionable insights to optimise your brand for Large Language Models like ChatGPT, Google SGE, Claude and Perplexity.
