Our team is developing a Generative AI tool for legal discovery, and we are stuck between two paths. Should we fine-tune a base model like Llama 3 on our specialized legal corpus to teach it "legalese," or is a RAG-based approach sufficient for extracting case law? We need high precision and the ability to cite specific sources. I’m worried that fine-tuning won't allow for easy source attribution, which is a dealbreaker for lawyers. Has anyone dealt with this trade-off in highly regulated industries?
3 answers
For legal work, RAG is almost always superior because of the citation requirement. Fine-tuned models "absorb" information into their weights, making it impossible to produce a verifiable link back to a specific page of a legal brief. However, you might consider a "Peft" or LoRA approach to fine-tune the model's style and vocabulary so it understands the nuances of legal formatting, while still using RAG to fetch the actual facts. This hybrid approach gives you the professional tone of a lawyer with the factual grounding and transparency of a search engine.
Have you looked into the hardware costs for this? Fine-tuning even a 7B parameter model requires significant VRAM; do you have the local GPU infrastructure or are you planning to use a cloud provider like Lambda Labs?
Stick with RAG for accuracy. Use a specialized "Legal-BERT" for your embeddings to ensure the search understands the legal relationship between terms like "tort" and "liability."
Excellent suggestion, Jennifer. Domain-specific embedding models are the "secret sauce" for RAG. It ensures the retrieval stage is actually picking up the right context before the LLM even sees it.
James, the hardware cost is actually why we're leaning toward RAG. We can run the retrieval on a standard server and just hit an API for the generation. For a startup, spending $5k a month on A100 instances just to keep a fine-tuned model's weights updated with new laws is a nightmare. RAG lets us update our knowledge base in seconds for pennies, which is crucial for staying current with new court rulings.