Writing Clean Code for API Development

If I were just starting out with APIs, these are the 10 rules I’d follow. These best practices will help you create simple, clear, and consistent APIs that are easy to use and understand. 1/ Keep It Simple   ↳ Use clear, concise endpoints that describe resources.   ↳ Avoid over-complicating; keep naming consistent and understandable.   ↳ Example: `/books` for all books, `/books/{id}` for a specific book. 2/ Use RESTful Design   ↳ Use standard HTTP methods: GET, POST, PUT, DELETE.   ↳ Name endpoints with nouns like `/users` or `/orders` for clarity.   ↳ Example: HTTP code 200 (success), 404 (not found), 500 (server error). 3/ Choose Standard Data Formats   ↳ Use JSON as it’s readable and widely supported.   ↳ Keep data formats consistent across endpoints.   ↳ Example: `{ "title": "To Kill a Mockingbird", "author": "Harper Lee" }`. 4/ Provide Clear Documentation   ↳ Document endpoints with detailed descriptions.   ↳ Provide request and response examples for easy usage.   ↳ Example: Explain `/users/{id}` with request/response samples. 5/ Implement Versioning   ↳ Include versioning in the URL to manage changes.   ↳ Allow for updates without breaking existing clients.   ↳ Example: `/v1/books` for version 1, `/v2/books` for an updated version. 6/ Ensure Security   ↳ Use HTTPS for data encryption.   ↳ Implement authentication and authorization mechanisms.   ↳ Example: OAuth 2.0 to secure user access to APIs. 7/ Handle Errors Gracefully   ↳ Use standard HTTP status codes like 400, 404, and 500.   ↳ Provide informative error messages to help resolve issues.   ↳ Example: `400 Bad Request` for invalid input, with a detailed error message. 8/ Optimize Performance   ↳ Use caching to store frequent responses and speed up access.   ↳ Apply rate limiting to control the number of requests a user can make.   ↳ Example: Cache popular books, limit requests to prevent server overload. 9/ Test Thoroughly   ↳ Conduct functionality, performance, and security testing.   ↳ Ensure different user scenarios are tested for reliability.   ↳ Example: Use automated tools for end-to-end testing before deployment. 10/ Monitor and Update   ↳ Monitor API performance and user activity continuously.   ↳ Update the API to address bugs or add features regularly.   ↳ Example: Use Prometheus to monitor latency and health. – P.S: What would you add from your experience?

I’ve reviewed close to 2000+ code review requests in my career. At this point, it’s as natural to me as having a cup of coffee. However, from a senior engineer to now an engineering manager, I’ve learned a lot in between. If I had to learn to review code all over again, this would be the checklist I follow (inspired from my experience) 1. Ask clarifying questions:      - What are the exact constraints or edge cases I should consider?      - Are there any specific inputs or outputs to watch for?      - What assumptions can I make about the data?      - Should I optimize for time or space complexity?  2. Start simple:      - What is the most straightforward way to approach this?      - Can I explain my initial idea in one sentence?      - Is this solution valid for the most common cases?      - What would I improve after getting a basic version working?  3. Think out loud:      - Why am I taking this approach over another?      - What trade-offs am I considering as I proceed?      - Does my reasoning make sense to someone unfamiliar with the problem?      - Am I explaining my thought process clearly and concisely?  4. Break the problem into smaller parts:      - Can I split the problem into logical steps?      - What sub-problems need solving first?      - Are any of these steps reusable for other parts of the solution?      - How can I test each step independently?  5. Use test cases:      - What edge cases should I test?      - Is there a test case that might break my solution?      - Have I checked against the sample inputs provided?      - Can I write a test to validate the most complex scenario?  6. Handle mistakes gracefully:      - What’s the root cause of this mistake?      - How can I fix it without disrupting the rest of my code?      - Can I explain what went wrong to the interviewer?      - Did I learn something I can apply to the rest of the problem?  7. Stick to what you know:      - Which language am I most confident using?      - What’s the fastest way I can implement the solution with my current skills?      - Are there any features of this language that simplify the problem?      - Can I use familiar libraries or tools to save time?  8. Write clean, readable code:      - Is my code easy to read and understand?      - Did I name variables and functions meaningfully?      - Does the structure reflect the logic of the solution?      - Am I following best practices for indentation and formatting?  9. Ask for hints when needed:      - What part of the problem am I struggling to understand?      - Can the interviewer provide clarification or a nudge?      - Am I overthinking this?      - Does the interviewer expect a specific approach?  10. Stay calm under pressure:      - What’s the first logical step I can take to move forward?      - Have I taken a moment to reset my thoughts?      - Am I focusing on the problem, not the time ticking away?      - How can I reframe the problem to make it simpler?  

Want your API to be understood and used by AI and Agents? Use structure & language correctly: ▶ Name entities consistently & use vocabulary appropriately ▶ Have code examples in common languages like Python and JS ▶ Illustrate workflow with OpenAPI Arazzo or Postman Collections ▶ Publish machine-readable schemas, like JSON Schema, XML or GraphQL ▶ Use accepted standards such as OPA, OTel, Problem JSON, ISO formats, and HTTP status codes ▶ Use API Description Languages, such as OpenAPI, AsyncAPI, or Proto3, and also the upcoming SLA4OAI spec Not only will it be helpful for agents to parse your API, but it will also allow you to build clients faster with tools. What other tools and standards have you seen working for LLMs? Let me know in the comments👇