AI Code Generation: Best Practices That Actually Work

AI code generation is a skill. The same tool produces excellent code for one developer and garbage for another. The difference is technique — how you prompt, what context you provide, and how you iterate.

After a year of daily AI-assisted coding, here are the practices that consistently produce better results.

1. Provide Full Context

The number one mistake: asking AI to generate code without sufficient context.

Bad:

Create a user profile component

Good:

Create a React server component for the user profile page.
Use the existing Card component from @/components/ui/card.
Fetch user data from Supabase using createClient from @/lib/supabase/server.
Display: avatar, display name, email, role badge, joined date.
Match the dark theme style used in app/tools/page.tsx.
Use Tailwind CSS classes consistent with the rest of the app.

The more specific your request, the better the output. Reference existing files, patterns, and styles. AI generates better code when it knows what "good" looks like in your codebase.

2. Start with the Interface

Define the interface before asking AI to implement it:

// Define what you want first
interface BlogPost {
  slug: string
  title: string
  excerpt: string
  content: string
  author_name: string
  published_at: string
  tags: string[]
}

// Then ask AI to implement:
// "Create a function that fetches published blog posts
// from Supabase and returns BlogPost[]. Handle errors
// by returning an empty array and logging the error."

When AI knows the types, it generates code that conforms to them. Without types, it guesses — and guesses wrong.

3. Use Examples as Prompts

The most effective prompt is an example of what you want:

I have this existing API route (app/api/reviews/route.ts):
[paste existing route]

Create a similar API route for blog posts at app/api/blog/route.ts.
Same pattern: GET for listing (with pagination), POST for creating (admin only).
Use the blog_posts table instead of reviews.

AI excels at pattern replication. Showing it an example of your coding style produces much more consistent results than describing the style.

4. Generate, Then Refine

Do not expect perfect output on the first generation. Use a two-step process:

Step 1: Generate the structure

Create a blog post page component with:
- Metadata generation from post data
- Markdown rendering
- Related tools section
- JSON-LD structured data

Step 2: Refine specific parts

The JSON-LD schema is missing the publisher field.
Add publisher with name "BuilderAI" and url "https://builderai.tools".
Also add dateModified using the post's updated_at field.

Big-picture generation followed by targeted refinement produces better results than trying to get everything right in one prompt.

5. Use Cursor's @ References

In Cursor specifically, use @ to reference files and documentation:

@app/tool/[slug]/page.tsx — Follow this page's pattern
@lib/seo.ts — Use these SEO helpers
@components/MarkdownContent.tsx — Use this for rendering

Create a blog post page at app/blog/[slug]/page.tsx that follows
the same patterns as the tool page.

Explicit references eliminate ambiguity and give AI exactly the context it needs.

6. Validate Immediately

Generated code should be validated within 30 seconds of generation:

1. TypeScript: Does it compile without errors?
2. Imports: Are all imports valid? AI sometimes imports non-existent modules.
3. Logic: Does the core logic make sense? Trace through the main path mentally.
4. Edge cases: What happens with null input? Empty arrays? Missing data?

The faster you catch errors, the faster you can iterate. Do not accept generated code without at least a quick review.

7. Write Tests First

Test-driven development works even better with AI:

// Write the test first
describe('calculateLeaderboardScore', () => {
  it('returns 100 for one submission', () => {
    expect(calculateLeaderboardScore(1, 0, 0)).toBe(100)
  })
  it('caps rating bonus at 500', () => {
    expect(calculateLeaderboardScore(0, 0, 100)).toBe(500)
  })
})

// Then: "Implement calculateLeaderboardScore to pass these tests"

AI generates correct implementations from test specifications because the expected behavior is explicit and unambiguous.

8. Keep Context Windows Clean

Long conversations degrade AI output quality. When the context gets cluttered:

• Start a new chat/conversation
• Provide only the relevant context for the current task
• Reference files explicitly instead of relying on conversation history

In Cursor, I start a new Composer session for each distinct task. In Claude, I start a new conversation. Clean context produces better output.

9. Know When to Write Manually

AI code generation is not always the right choice:

• Critical security logic: Write auth checks, encryption, and access control by hand
• Complex algorithms: If the logic is non-obvious, writing it yourself ensures understanding
• One-liners: If it takes longer to describe than to type, just type it
• Debugging AI output: If you have iterated three times and it is still wrong, write it yourself

The goal is speed, not delegation. Use AI where it accelerates you and code manually where it does not.

10. Build a Prompt Library

Save prompts that produce good results. My library includes:

• "Generate React server component following [file] pattern"
• "Create Supabase migration with RLS policies"
• "Write API route with error handling, auth check, and input validation"
• "Generate Playwright E2E test for [workflow]"
• "Review for security vulnerabilities: [checklist]"

Reusable prompts produce consistently good output because you have already refined them.

The Meta-Practice

The single most important practice is paying attention to what works. When AI generates great code, note why. When it generates garbage, note why. Over time, you build an intuition for effective AI-assisted coding that becomes automatic.

AI code generation is a new skill. Like any skill, it improves with deliberate practice.

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