Prompt Engineering Guide

How do I write better prompts for AI code generation?

Be specific about language, framework, and constraints. Include example inputs/outputs. Specify error handling expectations. Reference existing code patterns ("following the same pattern as UserService"). Break complex tasks into steps rather than asking for everything at once. The most common mistake: vague prompts like "make it better" instead of "refactor this function to use async/await and add error handling for network failures."

How do I write better prompts for AI code generation?

The best coding prompts are specific and contextual: include the language, framework, exact behavior you want, and any constraints (e.g., "Write a TypeScript function using Zod that validates an email and returns a typed Result type — no exceptions"). Providing an example of the pattern you want (few-shot prompting) dramatically improves output. Always specify edge cases you care about.

What is few-shot prompting and how does it help with code generation?

Few-shot prompting means giving the AI one or two examples of what you want before asking it to generate new code. For instance, showing it two existing functions in your codebase before asking it to write a third helps it match your naming conventions, error handling patterns, and style. Most AI coding tools support this by letting you include files as context.

How can I use system prompts to improve AI coding assistance?

A system prompt sets persistent instructions for every interaction — for example: "You are a TypeScript expert. Always use strict types, never use 'any', prefer functional patterns, and handle errors with Result types." Tools like Cursor allow custom system prompts per project via a .cursorrules file. This is one of the highest-leverage ways to improve AI output consistency across a codebase.