AI Prompt Engineering Best Practices Guide

What is Prompt Engineering?

The practice of writing effective inputs to LLMs to get reliable outputs. It directly impacts code quality, agent reliability, and development velocity.

TL;DR: Prompt engineering guide for AI coding tools. System prompts + few-shot + chain-of-thought + structured output + constraint prompting. Applies to Claude/GPT/Gemini. Must-read for CLAUDE.md and Cursor Rules.

Core Techniques

1. System Prompts

Set role and rules in CLAUDE.md or .cursorrules.

2. Few-Shot Learning

2–3 input/output examples often beat long instructions.

3. Chain-of-Thought

"Think step by step" boosts accuracy on complex reasoning.

4. Structured Output

Specify JSON/Markdown to ensure parseable results.

5. Constraint Prompting

Tell the model what not to do and define boundaries.

FAQ

Q: Do frontier models still need prompt engineering? A: Yes. Better prompts = more reliable output + fewer retries + lower cost.

Q: Different prompts for different models? A: Core techniques are universal; details tune to each model's quirks.