TL;DR: Good prompts are specific, provide context, and define success criteria. Use structured formats, examples, and constraints to get reliable outputs from AI models. Iteration beats perfection on the first try.
Why developers need prompt engineering
If you’re building with AI APIs or using coding assistants daily, prompt engineering isn’t optional—it’s a core skill. The difference between “write a function” and a well‑crafted prompt can mean:
- 10x fewer iterations to working code
- Outputs that match your style and architecture
- Consistent results across different runs
- Fewer hallucinations and off‑target responses
The anatomy of a good prompt
A solid prompt has four elements:
- Context: What the model needs to know (codebase info, constraints, style)
- Task: The specific goal, clearly stated
- Format: How you want the output structured
- Examples: Concrete illustrations of what good looks like
Basic pattern
# Context
You are a senior Python developer working on a FastAPI service.
Our style guide requires type hints, docstrings, and pytest tests.
# Task
Write a function that validates email addresses using regex.
# Format
Return the function definition, docstring, and 3 pytest test cases.
# Example
def validate_username(username: str) -> bool:
"""Check if username meets requirements."""
...Pattern library
1. Code generation with constraints
Write a React component for a loading spinner.
Requirements:
- Use TypeScript with strict types
- Accept size prop ("small" | "medium" | "large")
- Support custom color via CSS variable
- Include JSDoc comments
- No external dependencies
Return only the component code.Why it works: Explicit constraints prevent the model from adding unwanted libraries, styles, or complexity.
2. Debugging with context
This function is throwing "Cannot read property 'id' of undefined":
[paste your code]
Context:
- user object comes from authenticated middleware
- sometimes the error happens on logout
- happens in production but not locally
Identify the bug and suggest a fix with defensive checks.Why it works: Context about when/where the error occurs helps the model narrow hypotheses.
3. Code review with specific focus
Review this pull request for:
1. Security issues (SQL injection, XSS, auth bypasses)
2. Performance problems (N+1 queries, unnecessary loops)
3. Maintainability (naming, comments, modularity)
[paste diff]
Format: List issues as "File:Line - [Category] Description"Why it works: Focused criteria prevent generic feedback. Structured output is easier to act on.
4. Refactoring with style preservation
Refactor this function to improve readability without changing behavior.
Our style:
- Early returns over nested ifs
- Descriptive variable names (no abbreviations)
- Extract complex conditions into named booleans
[paste code]
Show before/after with comments explaining changes.Why it works: Style constraints keep the output aligned with your codebase conventions.
5. Test generation
Generate pytest test cases for this function:
[paste function]
Include:
- Happy path with typical inputs
- Edge cases (empty, null, boundary values)
- Error cases that should raise exceptions
- Parametrized tests where applicable
Use fixtures for common setup.Why it works: Explicit test categories ensure coverage of typical failure modes.
Anti‑patterns to avoid
❌ Vague requests
Make this better.
Optimize this code.
Fix the bug.Problem: Model has to guess intent. You’ll get generic improvements instead of targeted fixes.
❌ Missing context
Add authentication to this route.Problem: Model doesn’t know your auth stack (JWT? sessions? OAuth?), so it invents one.
❌ No constraints
Build a REST API for a todo app.Problem: You’ll get a kitchen‑sink implementation with a random framework, DB, and architectural choices.
❌ Overloading one prompt
Explain how this works, refactor it, write tests, document it, and suggest performance improvements.Problem: The model tries to do everything and does none of it well. Break into separate prompts.
Prompts for common dev tasks
API endpoint design
Design a RESTful endpoint for updating user profile settings.
Stack: Node.js + Express + PostgreSQL
Auth: JWT in Authorization header
Requirements:
- Partial updates supported
- Validate email format and required fields
- Return 400 for validation errors, 200 with updated user object
- Include TypeScript types for request/response
Show: route definition, handler, validation middleware, and response types.Database query optimization
This Prisma query is slow on large datasets:
[paste query]
Context:
- users table has 500K rows
- we filter by status and created_at often
- current indexes: id (PK), email (unique)
Suggest:
1. Missing indexes
2. Query rewrite if needed
3. Expected performance improvementDocumentation generation
Generate API documentation for this endpoint:
[paste code]
Format: OpenAPI 3.0 YAML
Include: path, method, parameters, request/response schemas, status codes, example request/response.Advanced techniques
Chain of thought prompting
For complex logic, ask the model to show its reasoning:
Analyze this algorithm's time complexity.
Show your work:
1. Identify loops and their bounds
2. Note any nested operations
3. State the final Big‑O with explanationFew‑shot learning
Provide 2–3 examples of input/output pairs:
Convert natural language to SQL queries.
Example 1:
Input: "Show me all users who signed up last week"
Output: SELECT * FROM users WHERE created_at >= NOW() - INTERVAL '7 days';
Example 2:
Input: "Count active subscriptions by plan"
Output: SELECT plan, COUNT(*) FROM subscriptions WHERE status = 'active' GROUP BY plan;
Now convert:
Input: "Find the top 5 customers by total spend this year"
Output:Role assignment
Frame the model as a specific expert:
You are a senior DevOps engineer specializing in Kubernetes.
Review this deployment YAML for production readiness:
- Resource limits and requests
- Health checks and probes
- Security contexts and policies
- High availability and scaling
[paste YAML]Iteration workflow
- Start simple: Get a working first pass
- Add constraints: Refine with style, performance, or security requirements
- Provide feedback: If output misses the mark, say why and re‑prompt
- Extract patterns: Save successful prompts as templates
Measuring prompt quality
Track:
- Acceptance rate: How often is the first output usable?
- Edit distance: How much do you change the generated code?
- Iteration count: How many back‑and‑forths to get it right?
Good prompts have high acceptance, low edits, and few iterations.
Tools and workflows
- Prompt libraries: Save proven templates in a snippets tool or repo
- Version control: Track prompt evolution alongside code
- A/B testing: Try variations and measure outcomes
- Logging: For production AI features, log prompts and outputs for debugging
When to use (and skip) AI code generation
Good fits:
- Boilerplate (CRUD handlers, type definitions, configs)
- Test scaffolding
- Data transformations with clear specs
- Documentation from code
- Refactoring with well‑defined rules
Poor fits:
- Novel algorithms requiring deep domain knowledge
- Security‑critical logic without expert review
- Highly stateful or concurrent systems
- Code that needs to integrate tightly with undocumented internals
Final thought
Prompt engineering is less about clever tricks and more about clear communication. Treat the model like a junior developer: give context, be specific, provide examples, and review the output carefully. The more you clarify upfront, the better the results—and the faster you ship.