That seems to me a sensible approach, because it gives lawyers the context to make it easy to review the result (from my limited understanding).
I wonder if much of what would want couldn't be achieved by analyzing and storing the text embeddings of legal paragraphs in a vector database, and then finding the top N closest results given the embedding of a legal question? Then its no longer a question of an LLM making stuff up, but more of a semantic search.
That seems to me a sensible approach, because it gives lawyers the context to make it easy to review the result (from my limited understanding).
I wonder if much of what would want couldn't be achieved by analyzing and storing the text embeddings of legal paragraphs in a vector database, and then finding the top N closest results given the embedding of a legal question? Then its no longer a question of an LLM making stuff up, but more of a semantic search.