The RAG technique
From RAG 101: Stop using “chatbots” and start building engines published on AI Lab, engineered by ActiveCampaign
Get the framework
The big idea
Retrieval-augmented generation (RAG) connects an AI model to a knowledge base you control, so it looks up relevant material before it responds instead of relying only on its general training data. The result is an answer grounded in your own documented thinking rather than generic industry knowledge—the difference between asking a colleague a question on the spot and giving them time to check their notes first.
The framework: how a RAG pipeline processes content
| Stage | What happens |
| Chunking | Content gets broken into smaller sections, or “chunks,” instead of being stored as whole documents |
| Embedding | Each chunk is converted into a vector, a format that lets the system compare ideas by meaning rather than exact wording |
| Storage and retrieval | Vectors are stored in a vector database (the source used Pinecone) built for semantic search, which finds the passages most relevant to a query |
| Generation | The retrieved passages are fed to the model as context, and it generates its response from that specific material |
When to use this
- You want AI answers grounded in your own published work instead of generic, broad-industry phrasing
- You’re losing documented thinking or past results between chat sessions and need it to persist
- You’re building a downstream tool, such as a query-response agent or a content-discovery workflow, that needs to pull from years of existing work instead of starting from a blank page
Common mistakes
- Dumping whole documents into the vector database: chunk content into logical sections first, so retrieval returns focused passages instead of noisy, partially-relevant blocks
- Treating chunking as one-size-fits-all: match your chunking strategy to content type, since what works for podcast transcripts may not work for documentation or support content
- Leaving the knowledge base static: build a self-updating ingestion pipeline so new content is embedded automatically instead of requiring a manual upload every time you publish
Quick-start
Pick one content type, such as blog posts or transcripts, and chunk it into short sections with slight overlap between them. Store the resulting vectors in a vector database, tagged with consistent metadata like source URL, publish date, and content type. Add more content types and automate the ingestion step once the first pipeline is working.
Related
More data from the AI Lab.