Fine-tuning LLMs: Solving Problems Prompt Engineering Can't
This article explores the practical applications of fine-tuning large language models (LLMs), particularly for problems that prompt engineering can't solve. Fine-tuning significantly improves model quality, such as improving task-specific scores, style consistency, and JSON formatting accuracy. Furthermore, it reduces costs, increases speed, and allows achieving similar quality on smaller models, even enabling local deployment for privacy. Fine-tuning also improves model logic, rule-following capabilities, and safety, and allows learning from larger models through distillation. However, the article notes that fine-tuning isn't ideal for adding knowledge; RAG, context loading, or tool calls are recommended instead. The article concludes by recommending Kiln, a tool simplifying the fine-tuning process.
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