Tip·July 22, 2026
Three checks before you fine-tune an LLM
A short tip list for teams deciding whether fine-tuning is the right move, or whether retrieval and better prompts come first.
Fine-tuning is powerful. It is also easy to waste money on.
Before you fine-tune
- Can retrieval solve it? If the knowledge changes often, RAG may beat a frozen fine-tune.
- Do you have labeled examples? Without a real evaluation set, you are guessing.
- Do you know the cost target? Serving a fine-tuned model in-house can win on privacy and unit economics, but only if volume justifies it.
Ship a thin baseline first. Fine-tune when the baseline's failure modes are clear and measurable.
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