Retrieval-Augmented Generation (RAG), Explained
Oct 21, 20261 min read
Summary
Retrieval-augmented generation connects language models to your own documents, so answers are grounded in current, private information instead of training data alone. It is often faster and cheaper than retraining, and retrieval quality is usually the biggest factor in answer quality.
Key takeaways
- 01Grounds answers in your own data
- 02Cheaper than retraining a model
- 03Retrieval quality drives answer quality
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Retrieval-augmented generation (RAG) is one of the most widely used patterns for building AI applications on private or fast-changing information.
The problem RAG solves
A language model only knows what it learned during training. It doesn't know your company's policies, your product documentation or yesterday's updates.
How RAG works
- Index: documents are split into chunks and converted into numerical representations called embeddings, stored in a search index or vector database.
- Retrieve: when a user asks a question, the system finds the most relevant chunks.
- Generate: those chunks are passed to the model with the question, and the model writes an answer grounded in that material.
Getting it right
- Chunking matters: too small loses context; too large adds noise.
- Combine search methods: pair semantic (embedding) search with keyword search.
- Evaluate continuously: check that the right passages are retrieved and answers stay faithful to them.
Key takeaways
- RAG grounds model answers in your own, current data.
- It is often faster and cheaper than retraining a model.
- Retrieval quality is usually the biggest factor in answer quality.
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