Engineering
When and How to Fine-Tune LLMs
Fine-tuning is rarely the answer. Here's the decision tree for your use case.
The fine-tuning trap
Teams assume fine-tuning solves hallucination, cost, or latency—it doesn't address root causes.
Fine-tuning is expensive, slow to iterate, and hard to roll back.
Decision tree
Domain knowledge? Use RAG + prompt engineering first.
Cost reduction? Optimize token usage, use smaller models, or batch requests.
Style consistency? Few-shot examples in the prompt is often enough.
When to fine-tune
Fine-tuning makes sense for format-specific tasks (classification, extraction) with 100+ labeled examples.
Use a small model (7B-13B), validate on holdout set, and monitor for distribution shift.
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