Fine-tuning
More training for an existing model on your own examples. It changes how the model behaves, not what it knows.
Fine-tuning keeps training a pretrained model on a dataset of your input and output pairs. It works for teaching a steady format, a house style or a narrow classification task, and it can let a small model match a large one on that single job at far lower cost.
It is a bad way to add facts. Knowledge baked in that way cannot be updated without retraining, and the model cannot cite it. Use rag for facts.
Try prompting first and push it hard. A few worked examples in the prompt, covered under few-shot-prompting, fix a surprising number of the problems that send people to fine-tuning, and they need no training run and no dataset to maintain.
Tagged
Where this comes up
Prompts, configs and tutorials in the library that touch this term.
- GuideUniversal
Turn a messy task into a prompt in five minutes
Most weak prompts come from a task nobody fully described. A five-step routine to fix that before you type, with a worked example.
- GuideUniversal
Summaries you can trust
A summary is a claim about a document, so it can be wrong in the same ways as any claim. Four habits keep it honest.
- GuideUniversal
Briefing a coding assistant: the first message
A coding assistant works from what you tell it and what it can see. Here is what to put in the first message so you spend less time correcting it.
- GuideUniversal
How to check what an AI tells you
A few checks that catch most wrong answers: which claims to verify, how to ask for sources, and why a confident tone tells you nothing.
- CheatsheetChatGPT
ChatGPT commands and keyboard shortcuts
The commands and shortcuts the interface never shows you, in one table. Mostly keyboard, mostly undocumented.
Tested on GPT-5 · May 2026
- GuideUniversal
How to write a prompt that actually works
A short checklist to run any prompt through before you send it: who, what, shape and what to leave out. Those four things separate a specific prompt…