Prompting techniques
Meta-prompting
Definition
Meta-prompting is using a model to write, critique or improve prompts. You describe the task and the failure modes you want to avoid, and the model drafts the prompt.
Models have absorbed an enormous amount of writing about how to instruct models, which makes them surprisingly good at prompt design. The most productive form is a three-step loop: ask for a draft prompt, ask the model to list that draft's weaknesses, then ask for a revised version addressing them.
This is how most people should bootstrap a prompt library. It is far faster than writing from scratch, and the critique step tends to surface missing constraints you would not have thought of.
The output still needs human judgement. Models are optimistic about their own prompts and will not know your specific edge cases unless you describe them.
Example
"Write a prompt that extracts structured data from job listings. Then list three ways your prompt could fail on messy real-world input, and produce an improved version."
Related terms
Prompt engineering
Prompt engineering is the practice of designing model inputs that reliably produce the output you want. It combines clear instruction-writing, structured formatting, worked examples, and systematic testing.
Prompt chaining
Prompt chaining splits a complex task into a sequence of focused prompts, where each step's output feeds the next. Chains are easier to debug and more reliable than one large prompt.
Chain-of-thought prompting
Chain-of-thought prompting asks a model to work through its reasoning step by step before answering. It measurably improves accuracy on maths, logic, and multi-step problems by forcing intermediate steps instead of an immediate guess.
Zero-shot prompting
Zero-shot prompting means asking a model to do a task with no worked examples — just an instruction. It works well for common tasks the model has seen extensively in training.
Few-shot prompting
Few-shot prompting means including two to five worked examples of the input-output pattern you want before making your real request. It is the fastest way to lock in a consistent format, tone or edge-case behaviour.
Tree-of-thought
Tree-of-thought extends chain-of-thought by exploring several reasoning branches in parallel, evaluating each, and pursuing the most promising. It helps on problems where the first plausible path is often wrong.
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