AI coding assistants have moved well beyond autocomplete — they can now help you scope requirements, generate boilerplate, write tests, and catch edge cases before you do. But dropping a vague prompt into ChatGPT and pasting the output straight into production is a recipe for subtle bugs and unreadable code. This tutorial walks you through a structured, repeatable workflow for building a real feature with AI assistance, using a user-facing search filter as the working example.
Step 1: Define the Feature Before You Touch the AI
The single biggest mistake developers make with AI assistants is starting too early. If your prompt is vague, the output will be vague. Spend five minutes writing a plain-English spec before you open any AI tool.
For this tutorial, the feature is: "Add a tag-based filter to a blog post listing page. Users can select one or more tags from a sidebar; the post list updates without a full page reload." That one sentence gives the AI enough context to generate useful, targeted code.
What a Good Mini-Spec Includes
User-facing behavior: Describe exactly what the user sees and does — clicks, inputs, and expected results.
Technical constraints: Note your stack (e.g., React 18, Next.js App Router, Tailwind CSS) so the AI doesn't hallucinate incompatible APIs.
Out of scope: Explicitly list what the feature does not need to do to prevent scope creep in generated code.
Acceptance criteria: One or two bullet points that define "done" — these double as test cases later.
Pro Tip: Paste your mini-spec directly into your first AI prompt. It acts as a system prompt that keeps every follow-up response on track.
Step 2: Use AI to Generate a Skeleton, Not a Final Solution
Ask the AI to produce a scaffold — the structure, file layout, and component signatures — rather than a complete, working implementation. This keeps you in control of the architecture while offloading the tedious parts.
A prompt that works well here: "Given this spec, outline the files I need to create or modify, the props each component should accept, and the shape of the filter state. Don't write full implementations yet." Review the outline critically before proceeding.
Reviewing the Scaffold
Check component boundaries: Make sure the AI hasn't collapsed two distinct concerns into one component, or split a simple thing into unnecessary pieces.
Verify state placement: Confirm that filter state lives at the right level in your component tree — AI often hoists state higher than necessary.
Spot hallucinated APIs: Cross-reference any library methods the AI mentions against the actual documentation before moving forward.
Step 3: Implement Section by Section with Targeted Prompts
Now implement the feature one slice at a time, prompting the AI for each discrete piece. Smaller prompts produce more accurate, reviewable output than asking for everything at once.
For the tag filter example, a good sequence is: (1) the TagFilter UI component, (2) the filter state hook, (3) the filtered post list component, and (4) wiring them together in the page. After each generation, read the code line by line — don't skip this step.
Prompt Patterns That Produce Better Code
Provide existing code as context: Paste in your current component or hook so the AI matches your naming conventions and style.
Ask for TypeScript types first: Request the interface or type definitions before the implementation — it forces the AI to think structurally.
Request inline comments: Ask the AI to comment non-obvious logic; it also helps you spot when the AI itself isn't sure what the code does.
Specify error handling explicitly: AI-generated code often omits loading states, empty states, and error boundaries unless you ask for them directly.
Important: Never paste AI-generated code into your codebase without reading it. AI assistants can produce plausible-looking code that silently fails under edge cases or misuses framework APIs.
Step 4: Use AI to Write and Refine Your Tests
Once the feature is working locally, prompt the AI to generate unit and integration tests based on your acceptance criteria. Paste in the component code and ask: "Write React Testing Library tests that cover the acceptance criteria I defined earlier, plus any edge cases you identify."
Review the generated tests the same way you reviewed the implementation — check that assertions are meaningful, not just checking that things render without throwing. A test that always passes is worse than no test at all.
Common Test Gaps to Watch For
Missing edge cases: AI tests often cover the happy path only — manually add tests for empty tag lists, all tags selected, and rapid state changes.
Shallow assertions: Check that tests verify actual DOM output or state values, not just that a function was called.
Accessibility checks: Ask the AI explicitly to add
getByRolequeries and keyboard interaction tests, which it skips by default.
Key Takeaways
Spec first, prompt second: A clear plain-English spec dramatically improves AI output quality and keeps the feature on track.
Scaffold before implementing: Ask for structure and component signatures first so you stay in control of the architecture.
Prompt in slices: Smaller, focused prompts produce more accurate and reviewable code than single large requests.
Read every line: AI-generated code must be reviewed critically — treat it as a junior developer's pull request, not a finished solution.
Close the loop with tests: Use AI to generate a test suite, then audit it for edge cases and meaningful assertions before shipping.
