Retrieval Augmented Generation (RAG) for Legal and Compliance
Retrieval Augmented Generation (RAG) is a technology area that combines the strengths of retrieval systems and generative language models. While generative language models create answers based on large data sets and statistical probabilities, retrieval systems use existing databases to retrieve relevant information. RAG unites these two approaches by first retrieving relevant documents from a database and then using the generative component to formulate a precise and contextually appropriate answer (see figure). This combination makes it possible to enhance both the depth and accuracy of the generated texts.
For legal and compliance professionals, RAG is particularly significant as it can enable more precise and efficient handling of legal questions and compliance requirements. Its ability to quickly retrieve relevant information and generate contextually appropriate answers can assist in analyzing complex legal documents, interpreting contract clauses, and assessing legal risks. The technical challenges in implementing RAG primarily lie in integrating and managing large and (potentially) heterogeneous data sets. Legal and compliance data are often stored in various formats and locations, making consolidation and access difficult. Moreover, the retrieved information must be of high quality and relevance to generate useful and accurate answers.
The combination of retrieval and generation capabilities in RAG could lay the foundation for true legal co-pilots, supporting legal and compliance professionals in their daily work. These co-pilots could function as intelligent assistants that not only provide information but also offer recommendations and support decision-making. By automating time-consuming tasks and providing well-informed, contextualized information, RAG could significantly enhance efficiency and accuracy in legal and compliance departments, paving the way for a new era in legal advisory and oversight.
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