Skip to content
AI Resource Hub
AI Coding

How to Use AI for Programming: A Practical Workflow

Use AI coding assistants to learn, write, debug and review code — and avoid shipping bugs and security holes.

By AI Resource Hub Editorial TeamUpdated 2 min read
On this page
  1. Choose how you'll use AI
  2. 1. Learn faster
  3. 2. Plan before generating code
  4. 3. Generate code in small pieces
  5. 4. Debug with full context
  6. 5. Write tests
  7. 6. Review everything
  8. Protect secrets and code
  9. Next steps

AI coding assistants can make you faster and help you learn, but they also produce plausible-looking code that's wrong. The developers who benefit most use AI as a collaborator and keep responsibility for every line.

Choose how you'll use AI

  • Chat assistants (ChatGPT, Claude, Gemini): explain concepts, write functions, debug errors.
  • Editor assistants (GitHub Copilot, Cursor): suggestions while you type, chat that knows your codebase, agents that edit multiple files.
  • Browser builders (Replit): build and host small apps from a description.

1. Learn faster

Ask for explanations at your level:

  • "Explain what async/await does in JavaScript with a simple example, then show a common mistake."
  • "I know Python. Explain how Go handles errors, comparing it to Python exceptions."

Then write code yourself and ask for a review.

2. Plan before generating code

For anything bigger than a function, describe the feature and ask for a plan first: data model, edge cases, tests. Our feature planning prompt helps. Generating code without a plan leads to tangled results.

3. Generate code in small pieces

Ask for one function or component at a time with clear inputs and outputs. Include:

  • Language and version.
  • Libraries allowed.
  • Examples of expected input and output.
  • Your project's conventions.

4. Debug with full context

Paste the full error, the relevant code, what you expected and what you've tried. See our debugging prompt. Ask for the most likely causes in order and how to verify each one.

5. Write tests

Ask AI to write tests covering edge cases. If a test fails, investigate before "fixing" the test – it may have found a real bug.

6. Review everything

Before you commit AI-written code:

  1. Read and understand every line.
  2. Run the tests and the app.
  3. Check for security issues: SQL injection, unvalidated input, exposed secrets, unsafe dependencies.
  4. Check that suggested packages actually exist and are maintained – AI can suggest packages that don't exist, which attackers sometimes exploit.

Protect secrets and code

Never paste API keys, passwords or proprietary code into tools your employer hasn't approved. Many business plans offer stronger data protections; check your organisation's policy.

Next steps

Compare editor tools in GitHub Copilot vs Cursor and browse our programming prompts.

Frequently asked questions

Will AI replace programmers?

AI changes how programmers work, but someone still needs to define problems, review code and take responsibility for systems.

Which AI coding tool is best for beginners?

A chat assistant for explanations plus a free tier of GitHub Copilot or Replit is a good start. Focus on understanding the code.

AI Resource Hub Editorial Team

Our editors research AI tools using official documentation and reliable reporting, and update guides when products change. Read our editorial policy to see how we work. Editorial policy.

Tools in this guide

Claude

Anthropic

AI assistant known for long documents, careful writing and coding help.

  • Analysis of long PDFs and documents
  • Projects for organising files and instructions
  • Artifacts for documents, code and interactive pages
Free plan
Read reviewOfficial site (opens in a new tab)

Related articles