Technique library
Prompting techniques that actually work
A prompting technique is a repeatable way of structuring what you ask an AI model so it gets the answer right more often. Each page below gives a plain definition, when to use it and when not to, a copyable template, a worked example, the common mistakes and the original research.
All techniques
Agents & loops
Advanced
Gauntlet Loop
A builder–critic agent loop: fresh critics judge each piece of work against a real reference and send the biggest gap back until the output wins.
Best for: Long agent runs on one ambitious, inspectable artifact
Reasoning
Beginner
Chain-of-Thought Prompting
Get the model to write out intermediate reasoning steps before its final answer.
Best for: Multi-step maths, logic and planning on non-reasoning models
Structure & format
Beginner
Few-Shot Prompting
Show the model a handful of worked input–output examples so it copies the pattern on a new input.
Best for: Locking down format, tone and labelling rules
Agents & loops
Intermediate
ReAct (Reason + Act)
An agent loop that alternates a written thought, a tool action and the observed result until it can answer.
Best for: Tool-using agents, research and multi-hop questions
Reasoning
Intermediate
Self-Consistency
Sample several independent reasoning paths and keep the answer most of them agree on.
Best for: Questions with one checkable answer where accuracy beats cost
Workflows
Intermediate
Prompt Chaining
Split a task into a sequence of prompts where each step’s output feeds the next.
Best for: Multi-stage work: research → outline → draft → edit
Structure & format
Beginner
Role (Persona) Prompting
Give the model an identity and audience to set its vocabulary, priorities and tone.
Best for: Tone, framing and domain priorities — not accuracy
Verification
Intermediate
Chain-of-Verification (CoVe)
Draft, write fact-check questions, answer them independently, then rewrite using what survived.
Best for: Cutting hallucinated facts in lists and long answers
Reasoning
Advanced
Tree-of-Thoughts (ToT)
Generate several candidate steps, score them, explore the best and backtrack from dead ends.
Best for: Puzzles, planning and search problems with dead ends
Workflows
Beginner
Meta-Prompting
Use a model to write, critique or improve prompts instead of writing every prompt by hand.
Best for: Drafting and improving prompts for repeated tasks
Start from your problem
Not sure which technique you need? Find the symptom.
| If this is happening… | Try |
|---|---|
| The output format or tone keeps varying | Few-Shot Prompting |
| Multi-step maths or logic comes out wrong | Chain-of-Thought Prompting |
| A one-off error slips through on hard questions | Self-Consistency |
| Lists and long answers contain made-up facts | Chain-of-Verification (CoVe) |
| The model needs to look things up or use tools | ReAct (Reason + Act) |
| One prompt is trying to do too many jobs | Prompt Chaining |
| Answers are pitched at the wrong level or audience | Role (Persona) Prompting |
| A puzzle or plan dies on an early wrong choice | Tree-of-Thoughts (ToT) |
| You don’t know how to write the prompt at all | Meta-Prompting |
| An agent should keep improving a big artifact | Gauntlet Loop |