Large language models (LLMs) are generalists by design. For highly specialized work — like insurance underwriting or claims processing — they often need fine-tuning to perform well.

Step 1: Define Your Objective

Before training, decide exactly what the model should do better. In my case, I needed improved accuracy in identifying risk factors from free-text descriptions.

Step 2: Gather Domain-Specific Data

• Start with anonymized real-world examples.
• Supplement with synthetic data that covers rare but important cases.

Step 3: Choose the Right Model & Method

• Small datasets: LoRA or adapter-based fine-tuning is cost-effective.
• Large datasets: Full fine-tuning may be worth it, but requires more compute.

Step 4: Evaluate Iteratively

Run evaluation after each training round. Focus on recall if missing details is costly, or precision if false positives are expensive.

Step 5: Deploy with Guardrails

Even a fine-tuned model can be wrong. Pair it with a retrieval system or rules engine for critical checks.

Final Thought

Fine-tuning is not about making the model “perfect” — it’s about making it consistently useful in your specific context.