Prompt Engineering for Code Generation

The difference between mediocre and excellent AI-generated code is almost always the prompt. After thousands of hours using LLMs for code generation, here are the techniques that consistently produce better results.

Be Specific About Context

Do not just say write a function to process users. Specify the framework (Next.js 15 with App Router), the language version (TypeScript 5.5 with strict mode), the database (PostgreSQL via Prisma), and the exact function signature you want. The more context you provide upfront, the less back-and-forth you need.

Use the Spec-First Pattern

Before asking for code, ask the AI to write a specification. Describe what you need, then say: before writing code, outline your approach including the data structures, error cases, and edge cases you will handle. Review the spec, suggest corrections, and then say proceed with the implementation. This two-step approach catches architectural mistakes before they are coded.

Provide Examples for Patterns

When you need code that follows a specific pattern in your codebase, include an example. Say: here is how we write API routes in this project, then paste an existing route. Now write a similar route for the orders endpoint. Few-shot examples are the most reliable way to get code that matches your project conventions.

Iterative Refinement Over One-Shot

For complex implementations, break the work into steps. Start with the data types, then the core logic, then error handling, then tests. Review each step before moving to the next. This produces better code than asking for everything at once because the AI can focus its attention on each aspect.

Testing Your Prompts

Keep a collection of prompts that worked well and reuse them as templates. When a prompt produces poor results, analyze why: was the context insufficient, were the requirements ambiguous, or was the task too complex for a single prompt? Treat prompt engineering as a skill that improves with deliberate practice, not a one-time setup.