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 varyingFew-Shot Prompting
Multi-step maths or logic comes out wrongChain-of-Thought Prompting
A one-off error slips through on hard questionsSelf-Consistency
Lists and long answers contain made-up factsChain-of-Verification (CoVe)
The model needs to look things up or use toolsReAct (Reason + Act)
One prompt is trying to do too many jobsPrompt Chaining
Answers are pitched at the wrong level or audienceRole (Persona) Prompting
A puzzle or plan dies on an early wrong choiceTree-of-Thoughts (ToT)
You don’t know how to write the prompt at allMeta-Prompting
An agent should keep improving a big artifactGauntlet Loop

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