Fine-tuning
Continuing a model's training on your own examples so its behavior changes — baked into the model, not supplied at answer time.
Reviewed July 10, 2026Stable
When not to use it
- To add knowledge. This is the most expensive misunderstanding in the field. Fine-tuning teaches behaviour and form, not facts. Facts go in the prompt or come from retrieval.
- Before you've exhausted prompting. A good prompt with a few examples solves a surprising share of what people reach for fine-tuning to fix, at zero training cost and no maintenance.
- When your data changes often. Every meaningful update means retraining, re-evaluating, and redeploying. That's a treadmill you have to keep running.
Reach for something else instead
- Few-shot prompting — put three good examples in the prompt. It's free, instant, and shockingly competitive.
- RAG when the real need was current or private information.
- LoRA and other parameter-efficient methods if you do need to fine-tune. Full fine-tuning of a large model is rarely the right first move on cost alone.
Read more on the blog
- What is fine-tuning? How to specialize an AI modelFine-tuning takes a general-purpose model and adapts it to your specific task by continuing its training on your examples. It is one of the three ways to shape a model, the most powerful and the most misused. Here is what it actually does, how LoRA made it cheap, and the honest answer to whether you should do it.
- How AI models are trained: from raw text to a system that helpsA language model isn't programmed, it's grown, in stages, from a firehose of text into a system that answers helpfully. The full modern pipeline: pretraining, supervised fine-tuning, preference alignment, and the reasoning training that defines 2026, what each stage does, why none can be skipped, and how the recipe changed.
- RAG vs fine-tuning: the decision, honestlyThe most common question in applied AI, answered without the hedging: when do you use retrieval, when do you fine-tune, when do you need both, and what almost every team gets wrong about the choice.
- What is a large language model (LLM)? Complete guideLarge language models are the systems behind ChatGPT, Claude, and Gemini, the technology that made AI feel like it could talk, write, and reason. Underneath, an LLM does one deceptively simple thing: predict the next token. This guide explains what an LLM is, how it is built from pretraining through alignment, how it runs, what it can do, and where it falls short.
Further reading
- Hu et al. (2022), LoRA: Low-Rank Adaptation of Large Language Models — why full fine-tuning is rarely the right first move.
- Howard & Ruder (2018), Universal Language Model Fine-tuning for Text Classification — the transfer-learning recipe that preceded the LLM era.
- Kirkpatrick et al. (2017), Overcoming catastrophic forgetting in neural networks — the failure mode that shows up after launch.
Primary sources, listed so you can check the claims on this page rather than take them on trust.
Where people go wrong
- Fine-tuning on too little data and calling the result overfitting. A few hundred well-chosen, consistent examples usually beat thousands of noisy ones.
- Losing general ability while gaining a narrow one. Models can forget how to do everything else — catastrophic forgetting is real and shows up after launch.
- Never building a held-out evaluation set, so "it feels better" is the only evidence the expensive thing worked.
At a glance
FieldLanguage & LLMs
Core ideachange behavior via extra training
Common methodLoRA (parameter-efficient)
Best forstyle, format, narrow skills
Not forfresh, changing facts
DifficultyAdvanced
Often compared with
RAG vs. Fine-tuning — knowledge at answer time vs. behavior changed in the model.
Where this sits
25 concepts come first. Understanding it opens up 15 more.
11Levelsteps in
25Needs firstconcepts
15Opens up5% of 310
3Areascrosses fields
Computed from the prerequisite graph, not assigned. How this works