Prompt engineering is the practice of writing instructions that get AI models to produce useful output. It’s the difference between “write me some code” (vague, bad results) and a structured prompt that consistently produces what you need.

Why it matters

The same model can give wildly different results depending on how you ask. A well-crafted prompt can make a $0.27/1M token model (DeepSeek) outperform a poorly-prompted $15/1M token model (Claude Opus).

Techniques that work

1. System prompts

Tell the model WHO it is and HOW to behave:

You are a senior TypeScript developer. Write clean, typed code with error handling. Use functional patterns. Add JSDoc comments.

2. Few-shot examples

Show the model what you want with examples:

Convert these descriptions to SQL:
"all users from Germany" → SELECT * FROM users WHERE country = 'DE'
"orders over $100" → SELECT * FROM orders WHERE total > 100
"active subscriptions" →

3. Chain-of-thought

Ask the model to think step by step:

Debug this error. Think through it step by step:
1. What does the error message mean?
2. What could cause it?
3. How to fix it?

4. Structured output

Request specific formats:

Respond in JSON: {"fix": "...", "explanation": "...", "confidence": "high|medium|low"}

For AI coding tools

Most AI coding tools handle prompt engineering for you — Claude Code, Aider, and Cursor all have optimized system prompts. But understanding the basics helps you write better instructions.

See our 5 AI Prompts That Work for Debugging for practical templates.

Cost impact

Better prompts = fewer tokens = lower costs. A concise, well-structured prompt can use 50% fewer tokens than a rambling one while getting better results. See our prompt caching guide for additional savings.

Related: 5 AI Prompts for Debugging · How to Reduce LLM API Costs · Best AI Coding Tools 2026 · Ai Glossary Developers