---
title: "What is fine-tuning?"
excerpt: "Further-training a base model on your examples to shape its behavior. Rarely the fix for 'the model doesn't know our stuff' — that's a retrieval problem."
definition: "Fine-tuning is the process of further-training a pretrained model on a curated dataset of examples to specialize its behavior, style, or format, baking the new pattern into the model's weights."
date: "2026-07-09"
lastModified: "2026-07-09"
author: "Teo Deleanu"
authorAvatar: "/team/teo.jpg"
tags: ["Fine-tuning", "AI Engineering", "Production AI"]
keywords:
  - "what is fine-tuning"
  - "fine-tuning vs rag"
  - "when to fine-tune an llm"
---

Fine-tuning takes a pretrained model and trains it further on your own examples, updating the weights so the new behavior becomes part of the model. It is how you teach a model a consistent format, tone, or task shape that prompting alone struggles to enforce.

## Why it matters in production

Fine-tuning is good at behavior and bad at knowledge. If you need every answer in a specific structure, or a smaller cheaper model to mimic a larger one's outputs on a narrow task, fine-tuning earns its keep. What it does not reliably do is teach the model facts — and confusing those two is the expensive mistake.

## The common mistake

Fine-tuning to fix "the model doesn't know about our product." That is a [retrieval](/glossary/retrieval-augmented-generation) problem, not a training one. Bake facts into weights and they go stale the moment your docs change, and you cannot cite a source. [RAG](/glossary/retrieval-augmented-generation) updates by re-indexing a file; fine-tuning updates by running a training job and re-validating the whole model. For knowledge, retrieval wins almost every time.

## How we actually use it

We reach for fine-tuning to lock in format and behavior, not to inject facts — and only after prompting and retrieval have hit a real ceiling, because it adds a training pipeline and a versioned artifact to maintain. When we do fine-tune, [evals](/glossary/llm-evals) are non-negotiable: a fine-tune can silently regress on cases outside its training set, and only a test set catches it. Changing the base model later is a full [model migration](/blog/surviving-a-model-migration), fine-tune included.