The Core Misconception
Many CTOs believe that to make an LLM "know" their data, they must Fine-Tune it. This is often wrong. Fine-tuning is for teaching a model a *behavior* (e.g., "speak like a lawyer"), while RAG (Retrieval-Augmented Generation) is for giving it *facts*.
The Experiment
We compared two approaches for a Legal Tech client wanting to query 50,000 contracts.
Approach A: Fine-Tuning (Llama-3 8B)
- Training Cost: $4,500
- Update Latency: Days (Retrain)
- Hallucination Rate: 12%
Approach B: RAG (Vector DB)
- Setup Cost: $200 (Embedding)
- Update Latency: Real-time
- Hallucination Rate: 2%
Our Recommendation
For 95% of enterprise use cases, RAG is the winner. It provides:
- Traceability: The model can cite exactly which document it used for the answer.
- Security: You can enforce ACLs at the retrieval layer (user A can't retrieve Document B).
- Freshness: New documents are available immediately after indexing.
Fine-tuning should be reserved for high-volume niche tasks where prompt engineering fails to produce the desired format, or for reducing token latency in massive-scale deployments.