Definition

What is Fine-Tuning?

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A training process that adjusts a pretrained language model's weights on a domain-specific dataset to change its default behavior, style, or task performance.

Fine-tuning is best suited to changing how a model behaves rather than what it knows. It reliably shifts output format, tone, and narrow task performance, but it struggles to reliably encode new factual knowledge. Research consistently shows RAG outperforms unsupervised fine-tuning for knowledge injection, which is why most production systems fine-tune for behavior and use retrieval for facts.

Related RAG (Retrieval-Augmented Generation) Context Engineering

Further reading

Articles about Fine-Tuning

RAG vs fine-tuning: when to use each

RAG vs fine-tuning: RAG wins for knowledge injection and freshness, fine-tuning wins for style and format. The right choice is a context engineering call.

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Agent Drift AI Agent AI Hallucination AI Second Brain Context as a Service Context Compression Context Container Context Drift Context Engineering Context Poisoning Context Portability Context Rot Context Window Epistemic Provenance Fine-Tuning MCP Resources MCP Server MCP (Model Context Protocol) Multi-Agent System Prompt Caching Prompt Engineering RAG (Retrieval-Augmented Generation) Semantic Search Structured Context Tool Poisoning Wire

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