AI code generation is a skill. The difference between developers who get mediocre AI output and those who get production-ready code comes down to technique. Here's how to consistently get quality results.
The Quality Gap#
Two developers use the same AI tool. One gets:
// Developer A's result
function getUsers() {
return fetch('/api/users').then(r => r.json());
}The other gets:
1// Developer B's result
2interface User {
3 id: string;
4 email: string;
5 name: string;
6 role: 'admin' | 'user' | 'guest';
7 createdAt: Date;
8}
9
10interface GetUsersOptions {
11 page?: number;
12 limit?: number;
13 role?: User['role'];
14 search?: string;
15}
16
17interface PaginatedResponse<T> {
18 data: T[];
19 meta: {
20 total: number;
21 page: number;
22 limit: number;
23 totalPages: number;
24 };
25}
26
27export async function getUsers(
28 options: GetUsersOptions = {}
29): Promise<PaginatedResponse<User>> {
30 const { page = 1, limit = 20, role, search } = options;
31
32 const params = new URLSearchParams({
33 page: String(page),
34 limit: String(limit),
35 ...(role && { role }),
36 ...(search && { search }),
37 });
38
39 const response = await fetch(`/api/users?${params}`);
40
41 if (!response.ok) {
42 throw new ApiError('Failed to fetch users', response.status);
43 }
44
45 return response.json();
46}Same task. Vastly different results. The difference? How they asked.
Principle 1: Provide Context#
AI doesn't know your codebase. Tell it.
Bad: No Context#
Write a function to fetch users
Good: Rich Context#
Write a function to fetch users for our Next.js 14 application.
Context:
- We use TypeScript with strict mode
- Our API returns paginated responses with { data, meta } structure
- We have a custom ApiError class for error handling
- We follow the pattern: async function, options object parameter
- All API endpoints are at /api/{resource}
Requirements:
- Support pagination (page, limit)
- Support filtering by role
- Support search by name/email
- Proper error handling
- Full TypeScript types
Principle 2: Specify Your Standards#
Every team has coding standards. Make them explicit.
Create a Standards Document#
1# Code Generation Standards
2
3## TypeScript
4- Use interfaces over types for objects
5- Export types from the same file as functions
6- Use readonly where applicable
7- Avoid 'any' - use 'unknown' if type is truly unknown
8
9## Functions
10- Use async/await over .then()
11- Destructure options in function signature
12- Provide default values for optional parameters
13- Maximum function length: 30 lines
14
15## Error Handling
16- Throw typed errors (ApiError, ValidationError)
17- Always handle network failures
18- Log errors with context
19
20## Naming
21- camelCase for functions and variables
22- PascalCase for types and classes
23- SCREAMING_SNAKE for constants
24- Descriptive names over abbreviations
25
26## Comments
27- JSDoc for public functions
28- Inline comments only for non-obvious logic
29- No commented-out codeReference this in prompts:
Following our TypeScript standards (strict mode, interfaces over types,
async/await, typed errors), create a function that...
Principle 3: Use Examples#
Show the AI what good looks like.
Pattern Demonstration#
Create a new API endpoint following this existing pattern:
// Existing pattern (users endpoint)
export async function GET(request: NextRequest) {
try {
const { searchParams } = new URL(request.url);
const page = parseInt(searchParams.get('page') ?? '1');
const limit = parseInt(searchParams.get('limit') ?? '20');
const users = await db.user.findMany({
skip: (page - 1) * limit,
take: limit,
orderBy: { createdAt: 'desc' },
});
const total = await db.user.count();
return NextResponse.json({
data: users,
meta: { page, limit, total, totalPages: Math.ceil(total / limit) },
});
} catch (error) {
return handleApiError(error);
}
}
Now create the same pattern for a "products" endpoint with these fields:
- id, name, price, category, inStock, createdAt
- Additional filter: category (string), inStock (boolean), minPrice, maxPrice
Principle 4: Iterate Incrementally#
Don't ask for everything at once.
Step-by-Step Generation#
Step 1: "Define the TypeScript interfaces for a shopping cart"
Step 2: "Now add the function to calculate cart totals,
using the interfaces above"
Step 3: "Add a function to apply discount codes,
integrating with the total calculation"
Step 4: "Now add the API endpoint that uses all of these"
This approach:
- Catches issues early
- Builds coherent code
- Easier to review and modify
Principle 5: Request Explanations#
Understanding why leads to better code.
Create a rate limiter for our API with the following approach.
After the code, explain:
1. Why you chose this algorithm
2. Trade-offs of this approach
3. Edge cases to consider
4. How to test this
The explanation often reveals issues:
1// AI-generated code
2export class RateLimiter {
3 private requests: Map<string, number[]> = new Map();
4
5 isAllowed(clientId: string, limit: number, windowMs: number): boolean {
6 const now = Date.now();
7 const timestamps = this.requests.get(clientId) ?? [];
8
9 // Remove old timestamps
10 const validTimestamps = timestamps.filter(t => now - t < windowMs);
11
12 if (validTimestamps.length >= limit) {
13 return false;
14 }
15
16 validTimestamps.push(now);
17 this.requests.set(clientId, validTimestamps);
18 return true;
19 }
20}
21
22/*
23Explanation:
24
25Algorithm: Sliding window log
26- Stores individual request timestamps
27- Counts requests within the window
28
29Trade-offs:
30- Pro: Accurate rate limiting, smooth distribution
31- Con: Memory grows with requests (stores all timestamps)
32- Con: Not suitable for very high request volumes
33
34Edge cases:
35- Memory leak: Need periodic cleanup of old entries
36- Clock skew: Issues in distributed systems
37- Burst handling: This allows bursts at window boundaries
38
39Testing approach:
40- Unit test: Basic allow/deny
41- Unit test: Window expiration
42- Load test: Memory usage under high volume
43- Integration test: With actual API endpoints
44*/The explanation reveals the memory issue—prompting a follow-up request for improvement.
Principle 6: Validate Output#
Never trust AI output blindly.
Validation Checklist#
1□ Types are complete and accurate
2□ Error cases are handled
3□ Edge cases are considered
4□ No hardcoded values that should be configurable
5□ Security: No SQL injection, XSS, etc.
6□ Performance: No obvious inefficiencies
7□ Matches existing code style
8□ Has appropriate test coverageAutomated Validation#
1// Create a validation script
2async function validateGeneratedCode(code: string) {
3 const checks = [
4 { name: 'TypeScript compiles', fn: () => tscCheck(code) },
5 { name: 'ESLint passes', fn: () => eslintCheck(code) },
6 { name: 'No any types', fn: () => !code.includes(': any') },
7 { name: 'Error handling present', fn: () => code.includes('catch') },
8 { name: 'No console.log', fn: () => !code.includes('console.log') },
9 ];
10
11 const results = await Promise.all(
12 checks.map(async check => ({
13 name: check.name,
14 passed: await check.fn(),
15 }))
16 );
17
18 return results;
19}Principle 7: Use Structured Prompts#
Consistent prompt structure yields consistent results.
The CRISPE Framework#
1**C**ontext: Background information
2**R**ole: Who the AI should act as
3**I**nstructions: What to do
4**S**tandards: Quality requirements
5**P**attern: Example to follow
6**E**xpectation: Output format
7
8Example:
9
10Context:
11We're building a SaaS billing system using Stripe. Our backend
12is Node.js with Express, TypeScript, and Prisma.
13
14Role:
15Act as a senior backend developer familiar with payment systems.
16
17Instructions:
18Create a webhook handler for Stripe subscription events that:
19- Handles subscription.created, subscription.updated, subscription.deleted
20- Updates our database subscription records
21- Triggers appropriate notifications
22- Logs events for debugging
23
24Standards:
25- Full TypeScript types
26- Comprehensive error handling
27- Idempotent operations (handle duplicate webhooks)
28- Follow our existing service pattern
29
30Pattern:
31[paste existing webhook handler example]
32
33Expectation:
34Provide the complete handler code, types, and a brief explanation
35of the idempotency approach.Principle 8: Chain Complex Tasks#
Break complex features into connected generations.
Feature: User Invitation System#
Prompt 1: "Design the database schema for user invitations"
→ Get schema, review, iterate
Prompt 2: "Create the TypeScript types matching this schema"
→ Get types, verify against schema
Prompt 3: "Create the invitation service with these methods:
createInvitation, acceptInvitation, cancelInvitation"
→ Get service, verify types are used correctly
Prompt 4: "Create the API endpoints using the invitation service"
→ Get endpoints, verify integration
Prompt 5: "Create the email templates for invitation notifications"
→ Get templates, verify they use correct data
Prompt 6: "Generate tests for the invitation service"
→ Get tests, verify coverage
Each step validates the previous, catching issues early.
Common Anti-Patterns#
Anti-Pattern 1: Vague Requests#
❌ "Make it better"
❌ "Add error handling"
❌ "Make it production-ready"
✅ "Add try-catch with typed errors for network failures and validation errors"
✅ "Add input validation for email format and password strength"
✅ "Add logging, rate limiting, and authentication checks"
Anti-Pattern 2: All-at-Once#
❌ "Create a complete user management system with auth, roles, invitations,
profile management, password reset, two-factor auth, and admin controls"
✅ Break into 8 separate, focused prompts
Anti-Pattern 3: No Review#
❌ Generate → Copy → Paste → Ship
✅ Generate → Review → Test → Iterate → Refactor → Ship
Anti-Pattern 4: Fighting the AI#
❌ "No, do it differently" (10 times)
✅ Provide a concrete example of what you want
✅ Start fresh with clearer instructions
Building Your Prompt Library#
Save effective prompts for reuse:
1// prompts/api-endpoint.md
2const apiEndpointPrompt = `
3Create a Next.js 14 API route handler for {resource}.
4
5Tech stack:
6- Next.js 14 App Router
7- TypeScript strict mode
8- Prisma ORM
9- Zod validation
10
11Requirements:
12- GET: List with pagination, filtering, sorting
13- POST: Create with validation
14- Full error handling with ApiError
15- Request logging
16
17Follow this pattern:
18{existingExample}
19
20Resource fields:
21{fields}
22
23Filter options:
24{filters}
25`;Measuring Success#
Track your AI code generation quality:
Metrics to track:
Acceptance rate:
- % of generated code used without modification
Iteration count:
- Average prompts needed per feature
Bug rate:
- Bugs found in AI-generated vs human-written code
Time savings:
- Time with AI vs estimated time without
Conclusion#
Quality AI code generation is about:
- Rich context
- Clear standards
- Good examples
- Incremental building
- Thorough validation
Master these techniques and AI becomes a genuine productivity multiplier—not just a fancy autocomplete.
Bootspring's AI agents are trained on production-quality patterns. Get code that's ready to ship, not just ready to compile.