Generate Production Code with AI Prompting Strategies

TL;DR

• Bad prompts produce bad code. Context-first prompting (stack, patterns, constraints, types) is the difference between toy code and production code
• Always specify error handling, edge cases, types, and test requirements in your prompt
• AI code generation works best as a dialogue, not a one-shot request - iterate with follow-up prompts
• Different tools need different prompting styles: Copilot responds to comments, Claude responds to detailed instructions, ChatGPT responds to structured templates

Table of Contents

• Why Most AI-Generated Code Is Bad
• The Context-First Principle
• Prompt Templates for Real Features
• Good Prompt vs Bad Prompt: Real Comparison
• Prompting for TypeScript Specifically
• Prompting for Tests
• Iterative Refinement
• Red Flags in AI-Generated Code
• Tool-Specific Prompting Tips
• Using AI for Code Review
• Frequently Asked Questions

Why Most AI-Generated Code Is Bad

The default output from any AI coding tool is a generic implementation that works in isolation but breaks in production. This is not the AI's fault. It is a prompting problem.

When you type "write a function to fetch user data," the AI has no idea about your tech stack, error handling conventions, type system, authentication mechanism, retry policy, or logging setup. So it generates a textbook-quality function with try/catch, a generic fetch() call, and console.log for errors. That is not production code.

Production code must handle:

• Network failures and timeouts
• Authentication token expiry and refresh
• Rate limiting and retry with backoff
• Type validation on the response
• Proper error propagation (not swallowed errors)
• Logging that integrates with your observability stack
• Cancellation when the component unmounts

None of these appear unless you ask for them explicitly.

The Context-First Principle

Before asking AI to write code, give it context about your project. Think of it like onboarding a new developer. You would not say "write the login feature." You would say "here is our stack, here is how auth works, here are our conventions, now build the login feature."

The context template:

Stack: Next.js 15 App Router, TypeScript strict mode, Prisma ORM, PostgreSQL
Auth: NextAuth.js v5 with JWT strategy
Error handling: Custom AppError class with error codes, never throw raw strings
API pattern: Server Actions for mutations, RSC for reads
Validation: Zod schemas for all inputs
Testing: Vitest for unit tests, Playwright for E2E
Logging: Pino logger, structured JSON output

Including this context at the start of every conversation (or in a system prompt) immediately elevates the quality of generated code.

Prompt Templates for Real Features

Here is a prompt template for generating a complete feature:

## Context
[Your stack context from above]

## Task
Implement [feature name]: [one-sentence description]

## Requirements
- Input validation using Zod
- Error handling using our AppError class
- TypeScript strict mode (no any, no type assertions unless justified)
- Server Action for the mutation
- Proper loading and error states in the UI

## Existing Patterns
[Show a small code example from your codebase that follows the pattern you want]

## Edge Cases to Handle
- [List specific edge cases]

## Tests Required
- Unit tests for the validation logic
- Unit tests for the business logic
- Test for each error case

This template gives the AI enough context to produce code that fits your existing patterns.

Good Prompt vs Bad Prompt: Real Comparison

Bad prompt:

Write a function to create a user in the database

Bad output:

async function createUser(name: string, email: string) {
  try {
    const user = await db.query('INSERT INTO users (name, email) VALUES ($1, $2)', [name, email]);
    return user;
  } catch (error) {
    console.log(error);
    throw error;
  }
}

Problems: raw SQL (not using an ORM), no input validation, no type safety on the return value, console.log for error handling, no duplicate email check.

Good prompt:

Using Prisma ORM with PostgreSQL, write a createUser function.
- Input: { name: string, email: string, role: 'user' | 'admin' }
- Validate input with Zod (name: 2-50 chars, email: valid format)
- Check for duplicate email before insert, throw AppError('USER_EXISTS', 409) if found
- Return the created user without the password field
- TypeScript strict mode, explicit return type
- Handle Prisma-specific errors (P2002 for unique constraint)

Good output:

import { prisma } from '@/lib/prisma';
import { z } from 'zod';
import { AppError } from '@/lib/errors';

const createUserSchema = z.object({
  name: z.string().min(2).max(50),
  email: z.string().email(),
  role: z.enum(['user', 'admin']),
});

type CreateUserInput = z.infer<typeof createUserSchema>;
type UserResponse = Omit<User, 'password'>;

export async function createUser(input: CreateUserInput): Promise<UserResponse> {
  const validated = createUserSchema.parse(input);

  const existing = await prisma.user.findUnique({
    where: { email: validated.email },
  });

  if (existing) {
    throw new AppError('USER_EXISTS', 409, `User with email ${validated.email} already exists`);
  }

  const user = await prisma.user.create({
    data: validated,
    select: {
      id: true,
      name: true,
      email: true,
      role: true,
      createdAt: true,
    },
  });

  return user;
}

The difference is entirely in the prompt quality, not the AI model.

Prompting for TypeScript Specifically

TypeScript prompts need explicit constraints because AI models default to loose typing:

TypeScript rules for this codebase:
- strict mode enabled (no implicit any)
- Never use 'any' type. Use 'unknown' with type guards instead
- No type assertions (as Type) unless there is a comment explaining why
- Use discriminated unions for state types
- Use generics where the function works with multiple types
- Use satisfies operator for type checking object literals
- All function parameters and return types must be explicitly typed

To generate TypeScript interfaces from JSON API responses, use our JSON to TypeScript converter. Paste a real API response to get accurate interfaces without writing them manually.

Prompting for Tests

Test generation is one of AI's strongest capabilities, but you need to specify what kind of tests:

Write tests for the createUser function using Vitest.

Test cases required:
1. Happy path: valid input creates user and returns without password
2. Validation: reject name shorter than 2 characters
3. Validation: reject invalid email format
4. Duplicate: throw AppError with code USER_EXISTS for duplicate email
5. Prisma error: handle P2002 unique constraint violation gracefully

Test setup:
- Mock prisma using vi.mock('@/lib/prisma')
- Use describe/it blocks
- Each test should be independent (no shared mutable state)
- Assert specific error codes, not just "throws an error"

Without these specifics, AI generates generic tests that assert the function "returns something" and "throws on error" without testing meaningful behaviour.

Iterative Refinement

Treat AI code generation as a conversation, not a single request:

Round 1: Generate the initial implementation Round 2: "Add retry logic with exponential backoff for the database calls" Round 3: "Add structured logging using our Pino logger" Round 4: "Add input sanitisation to prevent XSS in the name field"

Each round builds on the previous output. The AI maintains context from earlier rounds and applies changes incrementally. This produces better results than trying to specify everything upfront because you can review and correct course at each step.

Red Flags in AI-Generated Code

Before committing any AI-generated code, check for these issues:

1. Imports that do not exist: The AI may import from packages that are not in your dependencies or use non-existent module paths
2. Deprecated APIs: AI models are trained on historical code. They may use deprecated methods from older versions of your libraries
3. Swallowed errors: Empty catch blocks or catch(e) { console.log(e) } that hide failures
4. Hardcoded values: Magic numbers, URLs, or credentials embedded in the code
5. Missing null/undefined checks: Especially in TypeScript with strict null checks disabled in the AI's mental model
6. Over-engineered abstractions: AI tends to add unnecessary layers of abstraction. If a simple function will do, do not accept a factory-builder-strategy pattern
7. Copy-paste from training data: Code that includes comments like "TODO: implement this" or placeholder values from other projects

When validating JSON configuration files generated by AI, run them through our JSON formatter and validator to catch syntax errors before they cause runtime failures.

Tool-Specific Prompting Tips

GitHub Copilot

Copilot is triggered by code context. Write detailed comments above your function:

// Create a user in the database using Prisma
// Validate input with Zod (name: 2-50 chars, email: valid)
// Check for duplicate email, throw AppError('USER_EXISTS', 409)
// Return user without password field
// Handle Prisma P2002 unique constraint error
export async function createUser(input: CreateUserInput): Promise<UserResponse> {
  // Copilot generates from here
}

Claude (chat/API)

Claude responds well to structured, detailed prompts with explicit requirements. Give it your full context, constraints, and examples.

ChatGPT

ChatGPT responds well to role-based prompting: "You are a senior TypeScript developer working on a Next.js application. Follow these conventions..."

Using AI for Code Review

AI is useful for review, not just generation. Paste a code block and ask specific questions:

• "Does this function handle all error cases correctly?"
• "Are there any security vulnerabilities in this code?"
• "What edge cases could break this implementation?"
• "Does this TypeScript code have any type safety issues?"

This is more valuable than asking "is this code good?" which produces generic feedback.

For a detailed comparison of AI coding tools, see our Copilot vs Cursor vs Claude Code comparison. And for a hands-on review of Claude's agentic coding tool, check our Claude Code review.

The Debuggers integrates AI-assisted development into client projects, using these prompting strategies to accelerate delivery while maintaining production-quality standards.

Frequently Asked Questions

Which AI tool generates the best code?

Claude produces the best code for complex, multi-file tasks due to its large context window and strong reasoning capabilities. GitHub Copilot is fastest for line-by-line inline completion while you are actively coding. ChatGPT is best for explaining concepts and generating code with detailed explanations. The best approach is using multiple tools for their respective strengths rather than relying on a single one.

Can AI replace programming knowledge?

No. AI generates code based on patterns, not understanding. You need programming knowledge to write effective prompts, evaluate the generated code, identify bugs the AI missed, and integrate the code into your existing architecture. Developers who understand the fundamentals get dramatically better output from AI tools than those who treat them as black boxes.

How do I prevent AI from generating insecure code?

Explicitly include security requirements in your prompt: "validate all inputs, sanitise output for XSS, use parameterised queries (never string concatenation for SQL), hash passwords with bcrypt, validate JWT tokens server-side." AI models will generate insecure code by default because the training data contains more insecure code than secure code. Making security an explicit requirement shifts the output significantly.

Is AI-generated code copyrightable?

This is an evolving legal area. In most jurisdictions as of 2026, purely AI-generated code has unclear copyright status. Code that is substantially modified or guided by a human developer maintains the developer's copyright. For commercial projects, treat AI-generated code as a starting point that you refine, review, and take responsibility for. Always review your organisation's IP policy regarding AI-assisted development.

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Validating AI-generated configurations?

Use our free JSON Formatter to validate any JSON output from AI tools before committing. Paste your config files, API schemas, or data structures to catch syntax errors instantly.

Need help building an AI-enhanced development workflow? The Debuggers consults on AI-assisted development practices for engineering teams.