TL;DR: most underperforming LLM features lack context, not capability. No eval set, no fine-tuning. Prompt, retrieval, evals, then maybe the weights. When an AI feature underperforms, fine-tuning is often the first remedy on the table. It sounds like serious engineering: a dataset, a training run, a model that is "ours". In practice it is... Weiterlesen
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You probably don't need fine-tuning
TL;DR: most underperforming LLM features lack context, not capability. No eval set, no fine-tuning. Prompt, retrieval, evals, then maybe the weights. When an…