Few-shot prompting
Put two or three worked examples of the task in the prompt, on top of the description.
Few-shot prompting works because completing a pattern is what these models do best. Describing an output format in words leaves room to interpret it. Two examples leave none.
It pays off most when the format is unusual, the labels are yours and not standard ones, or the edge cases matter. Keep the examples short and make one of them a hard case. Keep them consistent too, because a single slip in your examples gets copied faithfully.
The price is context. Once the examples run past a few hundred tokens and the task has stopped changing, fine-tuning starts to look cheaper.
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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…