DEEP EXPLANATION

Fine-Tuning GPT Models: When It Pays Off and How to Ship Safely (EXPLAINED)

Project BasedGPTHard22 min read

**Decision** Try prompt + tools + RAG first; fine-tune if style/reliability gap persists and data exists.

TL;DR — Quick Answer

Fine-tune for consistent format/tone, domain vocabulary, or narrow task at scale when prompts fail eval gates — need hundreds+ quality examples, holdout evals, shadow deploy, rollback plan; often RAG beats fine-tune for knowledge.

The Interview Question

When should you fine-tune GPT vs prompt/RAG? Outline data prep, training, eval, and rollout.

Deep Explanation

Decision Try prompt + tools + RAG first; fine-tune if style/reliability gap persists and data exists.

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