Fine-Tuning

In Depth

Fine-tuning adapts a general-purpose LLM to your specific use case. By training on examples of desired behavior — correct tool calls, domain-specific responses, particular output formats — the model becomes more accurate and reliable for your agent's tasks. Fine-tuning can reduce the need for complex prompting, lower latency by using smaller models, and improve consistency. However, it requires quality training data and ongoing maintenance as requirements change. Many production agent systems combine fine-tuned models for specific tasks with general models for flexibility.

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