Your Dataset Teaches the Edge Cases You Remember to Include

Fine-tuning quality is often decided before training starts, in curation rather than in hyperparameters. The practical decision order is lighter-weight than most teams expect: start with prompting, add retrieval when the answer needs outside or changing information, and only fine-tune when you need a stable behavior pattern to show up repeatedly at production scale. OpenAI’s prompt engineering guide emphasizes clear instructions, message structure, and few-shot examples because many “model problems” are really instruction problems. The retrieval guide frames retrieval as semantic search over your own data, which is exactly what you want when the facts live outside the model or…

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