Prompt Engineering for Developers

What Is Prompt Engineering

Prompt engineering is the practice of designing inputs (prompts) to large language models (LLMs) to get desired outputs. For developers, it's the difference between "write me a button" (vague, generic result) and "create a reusable React Button component with variants primary, secondary, and ghost; support loading state and disabled; use Tailwind; match our design tokens in tokens.css" (specific, usable result).

It's not magic—it's clarity. LLMs perform best when given explicit instructions, context, and constraints. Learning to articulate what you want in a way models understand is a core skill for the AI-augmented development workflow.

Principles of Effective Prompts: Specificity, Context, Examples

Three principles consistently improve prompt quality:

Specificity. The more precise your request, the better the output. "Create a form" is ambiguous. "Create an accessible React form with name, email, and message fields; client-side validation with Zod; submit handler that posts to /api/contact" narrows the space and yields usable code.

Context. Models don't know your codebase, conventions, or constraints. Provide them. "We use React 18, TypeScript, and Tailwind. Our API returns . We prefer functional components with hooks." Context reduces hallucination and aligns output with your environment.

Examples (few-shot). Show, don't just tell. "Here's our existing Card component [paste example]. Create a similar Table component following the same patterns." One or two examples establish style, structure, and conventions the model can mimic.

Zero-Shot, Few-Shot, and Chain-of-Thought Prompting

Different prompt strategies suit different tasks:

Zero-shot. You ask for something with no examples. Works well for straightforward tasks: "Convert this JSON to TypeScript interfaces." Use when the task is clear and the model has strong prior knowledge.

Few-shot. You provide 1–3 examples of input-output pairs. "Here are two API responses [examples]. Generate the TypeScript type for this third response [input]." Few-shot dramatically improves consistency for format-specific or convention-specific tasks.

Chain-of-thought (CoT). You ask the model to reason step by step: "Think through this step by step. First, identify the bug. Then, explain why it occurs. Finally, suggest a fix." CoT improves performance on logical, multi-step, or debugging tasks. Explicitly requesting reasoning often yields better final answers.

Using AI for Code Generation Effectively

AI excels at generating boilerplate, repetitive code, and common patterns. Use it for components, utilities, tests, and configuration—but always review and adapt.

Iterate. First output is rarely perfect. Refine: "Add error handling," "Use our custom useAuth hook instead of hardcoded user," "Make it responsive for mobile." Treat the first response as a draft.

Constrain the scope. "Generate only the component, no styling" or "Just the types, no implementation" keeps output focused and easier to integrate.

Verify and test. AI can introduce subtle bugs—wrong types, missing edge cases, or outdated APIs. Run the code. Write or run tests. Don't ship blindly.

Using AI for Debugging and Code Review

AI can help diagnose issues and suggest fixes. Paste the error message, relevant code, and context. "This React component re-renders on every parent render. Here's the code [paste]. Why and how do I fix it?"

Provide full context. Stack traces, environment details, and what you've already tried help the model avoid suggesting things you've already ruled out.

Ask for explanation, not just fix. "Explain why this happens" builds your understanding. "Fix it" might work but leaves you dependent. Understanding + fix is the goal.

Use AI for code review. "Review this PR for security issues, performance concerns, and alignment with our React patterns." AI catches common issues; human review catches nuance, business logic, and architectural fit.

Prompt Patterns for Frontend Tasks

Component generation. Specify: framework, styling approach, props/API, accessibility requirements, state (loading, error). Example: "React + Tailwind. Props: items (array), onSelect (callback). Loading skeleton. aria labels. Keyboard nav."

CSS and layout. Describe the desired layout: "Flexbox, 3-column grid on desktop, 1 column on mobile. 16px gap. Sticky header." Include design tokens or constraints if relevant.

Testing. "Generate Vitest unit tests for this React component. Mock useRouter. Test: renders with props, handles click, shows loading state." Specify test framework and what to cover.

Refactoring. "Refactor this to use the Compound Component pattern. Keep the same public API." Paste the original and describe the target pattern.

Limitations and When Not to Trust AI Output

AI makes mistakes. It can hallucinate APIs, use deprecated patterns, or miss edge cases. Treat it as a helpful assistant, not an oracle.

Don't trust without verification. For security-sensitive code, critical paths, or compliance-relevant logic, verify thoroughly. AI suggestions can introduce vulnerabilities.

Be wary of recency. Models may not know the latest library versions or recently changed APIs. Check documentation when integrating AI-generated code.

Recognize when not to use AI. Complex architectural decisions, nuanced product logic, or domain-specific knowledge often require human judgment. Use AI for acceleration, not replacement of thinking.