What This Research Area Covers
RAG research studies techniques for letting a model retrieve relevant external information at query time rather than relying purely on what it learned during training, aimed at improving factual grounding and enabling up-to-date, private, or domain-specific knowledge.
Why It Matters
Models have a fixed training cutoff and no access to private data by default; RAG addresses both by grounding responses in retrieved, verifiable source material.
Current Research Directions
Improving retrieval quality and relevance ranking, better handling of very large document collections, and techniques for helping a model appropriately weigh retrieved context against its own training knowledge are all active areas.
Related Pages
Frequently Asked
What's the difference between RAG and fine-tuning?
Fine-tuning changes a model's weights through additional training; RAG leaves the model unchanged and retrieves external content at query time — see our Fine-Tuning vs RAG page.
Does RAG eliminate hallucination?
It significantly reduces the risk by grounding answers in retrieved content, but doesn't eliminate it entirely — the model can still misinterpret retrieved material.
What role do vector databases play in RAG?
They store and enable fast similarity search over document embeddings, the core mechanism behind RAG's retrieval step — see our Vector Databases page.
Where can I learn the basic concept?
See our RAG Explained page.