Definition

What is Fine-Tuning?

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 concepts
RAG (Retrieval-Augmented Generation)

A technique that retrieves relevant documents or data at inference time and injects them into the model's context window before generating a response.

Context Engineering

The practice of deliberately designing, structuring, and managing the information provided to AI models to improve output quality and relevance.

Further reading
RAG vs Fine-Tuning: When to Use Each

All terms

View full glossary
AI Agent Context as a Service Context Container Structured Context Context Engineering Context Portability Context Rot Context Window MCP (Model Context Protocol) MCP Server RAG (Retrieval-Augmented Generation) Fine-Tuning Semantic Search Multi-Agent System Context Compression Context Drift Context Poisoning Prompt Caching Wire

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