Retrieval-Augmented Generation (RAG)

A standard large language model generates answers based entirely on patterns learned during training. RAG adds a retrieval step: before the model generates a response, a search system finds the most relevant documents from a curated corpus and injects them into the model's context. The model then generates its answer based on this retrieved evidence.

For legal work, this architecture is transformative. Instead of asking a model what it remembers about a legal topic, RAG ensures the model is looking at actual case law, statutes, or internal firm documents when formulating its response. This dramatically reduces hallucination risk and allows the system to cite specific sources that the lawyer can verify.

The quality of a RAG system depends heavily on the retrieval layer. A system that retrieves irrelevant documents will produce poor answers regardless of how capable the language model is. Legal RAG systems must handle the nuances of legal citation, distinguish between binding and persuasive authority, and understand jurisdictional relevance. The embedding models and search algorithms used for retrieval must be tuned for legal language, which differs substantially from general English.