Prompt Engineering Basics: Getting Reliable Output from Models
Published 2026-10-23
Prompts are spec docs, not spells
Writing a prompt is closer to briefing a new intern than casting a spell: the more specific the task, the richer the context, and the clearer the format, the more stable the output. Prompt engineering translates fuzzy intent into unambiguous instructions.
Five techniques you can use today
- Give a role: "You are a backend engineer with 10 years of experience" beats "help me code" by a mile.
- State task and audience: what to do, for whom, in what scenario. Miss one and the model guesses.
- Show examples (few-shot): pasting 1–3 input→output pairs is the strongest formatting control.
- Constrain the output: lock structure with JSON Schema, Markdown tables or fixed section names for downstream processing.
- Ask for reasoning: for complex problems, "analyze step by step, then conclude" usually improves accuracy (chain-of-thought).
A reusable template
Role: {who}. Task: {what}. Context: {background}. Requirements: {constraints and style}. Output format: {structure}. Examples: {1–3 samples}.Then self-check: remove any sentence and the result gets worse? Keep it. If not, delete it — prompts reward information density.
The debugging loop
- Change one variable at a time, or you won't know what worked.
- Save prompts that work (see the curated Awesome ChatGPT Prompts).
- Evaluate against a fixed test set, not vibes.
Further learning
- Prompt Engineering Guide (Chinese): the most systematic free tutorial.
- OpenAI's Prompt Engineering Guide: six official strategies.
- Learn Prompting: community course in English.
- DeepLearning.AI short course: free video course by Andrew Ng's team.