Prompt Engineering Cheat Sheet

The patterns, structures and fixes that reliably improve output

A one-page reference for writing prompts that work: the anatomy of a strong prompt, the six patterns worth memorising, ready-to-copy templates, and the mistakes that quietly ruin results.

Category: Prompt Engineering Difficulty: Beginner Version: 1.0 Updated: August 4, 2026 Author: Sabir

Overview

A prompt is an instruction, not a wish. The single biggest improvement most people can make is to state the ROLE, the TASK, the FORMAT and the CONSTRAINTS explicitly instead of hoping the model infers them.

Anatomy of a prompt

Include these in roughly this order. You rarely need all six, but naming them stops you forgetting the one that matters.

Part	What it does	Example
Role	Sets voice, expertise and assumed audience	You are a senior technical editor.
Task	The single thing you want done	Rewrite the paragraph below for clarity.
Context	Facts the model cannot guess	The audience is non-technical hospital staff.
Format	Exact output shape	Return a markdown table with 3 columns.
Constraints	Hard limits and exclusions	Under 120 words. No marketing language.
Examples	One or two worked cases	Input: ... Output: ...

Patterns worth memorising

Pattern	When to use it	Cue phrase
Zero-shot	Simple, well-known tasks	Just ask directly
Few-shot	Output must match a specific style	Here are two examples: ...
Chain-of-thought	Multi-step reasoning or maths	Work through this step by step
Role prompting	Domain expertise or tone matters	You are a ...
Structured output	The result feeds another system	Return valid JSON matching this schema
Self-critique	Accuracy matters more than speed	Review your answer and list any errors

Common mistakes

Each of these silently degrades output. Check them before blaming the model.

Asking two things at once — Split into separate prompts — quality drops sharply on compound asks.
Describing what you do not want — State the positive instruction; negations are followed unreliably.
No output format — If you did not specify the shape, you cannot complain about the shape.
Burying the instruction — Put the task at the top; long preambles push it out of attention.
Vague quality words — "Good", "professional" and "engaging" mean nothing — give a measurable rule.
No examples for style work — One example beats three paragraphs of description.

Prompt templates

Universal rewrite prompt

A dependable starting point for any "make this better" task.

SYSTEM

You are a meticulous editor. You preserve the author's meaning and voice. You never add facts that are not present in the source.

USER

Rewrite the text below for {{goal}}.

Audience: {{audience}}
Tone: {{tone}}
Hard limit: {{limit}}

Return only the rewritten text, with no preamble.

---
{{text}}

Variable	Description	Example
`{{goal}}`	What you want improved	clarity and brevity
`{{audience}}`	Who will read it	first-time customers
`{{tone}}`	Desired register	plain, warm, non-salesy
`{{limit}}`	A measurable constraint	under 100 words
`{{text}}`	The source text	Paste the paragraph here

EXAMPLE OUTPUT

Your booking is confirmed for Tuesday at 2pm. We will send a reminder the day before. If you need to change it, reply to this message and we will sort it out.

If the result drifts, add: "Do not change any names, dates or numbers."

Structured extraction prompt

Turns messy text into machine-readable JSON.

SYSTEM

You extract data. You return valid JSON only, with no commentary and no markdown fences.

USER

Extract the fields below from the text.

If a field is absent, use null. Never guess.

Schema:
{{schema}}

Text:
{{text}}

Variable	Description	Example
`{{schema}}`	The JSON shape you want	{"name": string, "date": string\|null}
`{{text}}`	Source content	Paste the email here

EXAMPLE OUTPUT

{"name": "Priya Raman", "date": "2026-03-14"}

Pair this with the API's JSON mode where available — the instruction alone is not a guarantee.

FAQs

Does prompt length matter?

Only insofar as it costs tokens and can push your instruction out of focus. A precise 60-word prompt usually beats a rambling 400-word one.

Should I use "please" and "thank you"?

It makes no measurable difference to quality. Use whatever register you are comfortable with.

Why does the same prompt give different answers?

Most models sample randomly. Set temperature to 0 (or the lowest available) when you need repeatable output.

Resources

AI Tools directory — /ai-directory
Request a free tool — /request-tool