RAG & Knowledge Systems
Giving your AI access to your own data. Retrieval-augmented generation, embeddings, vector search.
Retrieval-Augmented Generation means your AI searches a knowledge base before generating a response. This solves three problems: the model's knowledge cutoff, hallucination, and the fact that it doesn't know your proprietary data.
Do you actually need RAG?
You need RAG when: your product references frequently updated information, proprietary data, accuracy on specific facts matters, or users want citations.
You probably don't need RAG when: the model's built-in knowledge is sufficient, you're doing creative generation, or your context fits in the context window.
Context stuffing before RAG. If your reference material is under 100 pages and doesn't change often, just paste it into the prompt. No vector database needed. Trade money for simplicity until simplicity stops scaling.
The RAG pipeline
- Ingest. Load your documents. Parse and clean them.
- Chunk. Split into smaller pieces (200-1000 tokens with 10-20% overlap).
- Embed. Convert each chunk into a vector.
- Store. Put vectors in a vector database (Pinecone, Chroma, pgvector).
- Retrieve. Search for the most similar chunks to the user's question.
- Generate. Feed retrieved chunks plus the question to the model.
The validation question: Before building an elaborate RAG system, go back to your users. What questions do they actually ask? Load that specific content first.