Retrieval-Augmented Generation (RAG)
Definition: Retrieval-Augmented Generation (RAG) means an AI method that lets a large language model access and use external documents or data sources before it generates its answer. In simple terms, instead of relying only on what the AI “remembers,” the system first finds real, relevant information and then uses that to create a more accurate response.
Example
If a lawyer asks, “What recent changes were made to EU data-protection law?”, a RAG system would first fetch the latest regulation texts from the law firm’s internal database, then use that fresh material to craft a precise answer rather than guessing from old training data.
Why It Matters?
RAG matters in legal AI because it helps ensure that an AI’s output is grounded in actual, up-to-date legal sources, which reduces the risk of incorrect or made-up answers. For law firms and compliance professionals, this means better trust in AI tools, fewer hallucinations, and stronger defensibility of AI-generated insights.
How to Implement?
To implement Retrieval-Augmented Generation (RAG), start by creating a secure place for your data, such as a database or document library that the AI can search. Then connect this database to your AI system so it can retrieve relevant information before writing an answer. When someone asks a question, the AI first looks for supporting documents, gathers key details, and then uses those details to generate a complete response.
In a legal setting, this might mean connecting the AI to a law firm’s internal knowledge base, case summaries, or statutes. Each answer the AI gives would then be supported by real documents rather than memory alone, reducing hallucinations and improving accuracy.