We are building an internal AI for our legal department. We aren't sure if we should "Fine-Tune" a model on our contracts or use "Retrieval-Augmented Generation" (RAG) to pull relevant clauses into the prompt. Which approach is more cost-effective and accurate for a domain where the data changes weekly?
3 answers
For a legal department where data changes weekly, RAG is the clear winner. Fine-tuning is like "memorizing" a textbook; if the laws change, you have to reprint (re-train) the whole book, which is expensive and slow. RAG is like giving the model "open-book" access to your latest files. You store your contracts in a Vector Database like Pinecone or Milvus, and the model "searches" for the right clause before answering. This significantly reduces "hallucinations" because the model has to cite the source text provided in the context window.
How do you handle the "context window" limits if the legal documents are hundreds of pages long and contain very complex cross-references?
Go with RAG for the "Ground Truth." Fine-tuning is better for changing the style of the output, not for teaching the model new facts.
I agree with Susan. In a high-stakes field like law, you need the factual accuracy and transparency that only a RAG architecture can provide.
David, that’s where "Recursive Character Splitting" comes in. You don't just dump the whole document; you break it into 1,000-character chunks with a small overlap. To answer your concern about cross-references, we use "Parent Document Retrieval." This retrieves the specific chunk but provides the model with the broader context of the surrounding paragraphs. It allows the AI to understand that "Clause A" in the summary actually refers to the definition found on page 50, without hitting the token limit.