Retrieval-Augmented Generation (RAG)¶
Retrieval-augmented generation (RAG) is a technique that combines document retrieval with LLM generation — the model first searches a knowledge base for relevant information, then generates a response grounded in those retrieved documents.
Context & Background¶
RAG addresses a key limitation of LLMs: their tendency to hallucinate or rely on potentially outdated training data. By retrieving relevant documents before generating a response, RAG systems can:
- Ground responses in sources: Cite specific documents rather than generating from memory
- Access current information: Work with documents newer than the model's training cutoff
- Reduce hallucination: Constrain the model to information actually present in retrieved documents
- Enable domain expertise: Give the model access to specialized research papers or datasets
Practical Implications¶
- Use RAG for literature-heavy tasks: When accuracy of citations matters, RAG systems outperform vanilla LLMs
- Curate your knowledge base: RAG is only as good as the documents it can retrieve
- Verify attribution: Check that the model's claims actually appear in the cited sources
- Consider tools like NotebookLM: Purpose-built RAG tools for research document analysis