Comparison
BM25 vs Retrieval-Augmented Generation
BM25 and Retrieval-Augmented Generation are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for BM25
BM25 comes up when the question is fundamentally about agents & tools.
A codebase search where BM25 finds every file containing the exact function name; vector alone often missed them.
When you would reach for Retrieval-Augmented Generation
When the model needs information that is not baked into its weights — fresh, private, or domain-specific.
"Chat with your PDFs" — Notion, Glean, ChatGPT custom GPTs.
Frequently asked
What is the difference between BM25 and Retrieval-Augmented Generation?
BM25: BM25 is the classical keyword-based ranking algorithm: a refined TF-IDF that scores documents by query-term frequency, document length, and corpus-wide rarity. The keyword side of hybrid search. Retrieval-Augmented Generation: RAG retrieves relevant documents from a corpus at query time and includes them in the prompt, letting an LLM answer with up-to-date, source-cited, private information without retraining.
When should I use BM25 vs Retrieval-Augmented Generation?
BM25 is the right concept when you are focused on agents & tools. When the model needs information that is not baked into its weights — fresh, private, or domain-specific.
Are BM25 and Retrieval-Augmented Generation the same thing?
No. BM25 is agents & tools; Retrieval-Augmented Generation is agents & tools. They are related but address different parts of the AI stack.