AI learning guide
Understand retrieval-augmented generation
See how retrieving reference material helps generation and where it can fail.
By SuperAIGuy.com · AI-assisted educational content · Updated
Retrieval-augmented generation, or RAG, combines finding relevant material with generating a response. Instead of relying only on learned model parameters, a system retrieves information and supplies it as context for an answer.
A typical workflow
- Prepare documents and preserve their source details.
- Retrieve passages relevant to the question.
- Provide the passages to the model with clear instructions.
- Generate an answer and trace its claims back to the material.
Where errors enter
The system can miss a relevant document, retrieve stale material, or select a passage that lacks necessary context. Anthropic's Contextual Retrieval work illustrates how separating text into chunks can lose important meaning, and how adding document context may help retrieval. The model can still misread the retrieved passage or make an unsupported inference.
Evaluate retrieval and answers separately
Check whether the right evidence was retrieved, then whether the final answer used it faithfully. Include questions whose answers are absent from the collection. A citation is useful only when it supports the associated claim.
RAG is a design approach, not a guarantee of accuracy. The chat on this site does not automatically browse the web or retrieve this site's source library.
Put it into practice
Sources to check
- Introducing Contextual Retrieval · Anthropic Engineering · Link checked
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks · Lewis et al. · NeurIPS 2020 / arXiv · Link checked
Check dates record source-link reviews. These are source-based explanations prepared with AI assistance; no independent expert review is claimed. Read our method and limitations.