Definition

What is Fine-Tuning?

Last updated

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, long context, or fine-tuning: how to choose

RAG, long context, or fine-tuning? A 2026 decision guide on cost, accuracy, and freshness, with a use-case table for choosing the right one in production.

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.

All terms

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Agent Drift AI Agent AI Second Brain AI Hallucination Context as a Service Context Compression Context Container Context Engineering Context Drift Context Offloading Context Poisoning Context Rot Context Portability 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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