What are the best practices for using code assistants?
Here are five proven strategies every developer should follow:
1. Break Down Big Tasks
How do I structure prompts for AI coding assistants?
AI assistants work best with narrow, well-defined tasks. For example:
- Good: “Write a function that calculates tax brackets based on income range.”
- Bad: “Build the tax system module.”
Break work into steps like:
- Add a function to handle X
- Update the service layer
- Create a REST API endpoint
Verify results between steps. Use assistants planning function to verify the steps that agent is planning to take. If a step fails, it is easier to isolate and fix.
2. Give Relevant Context
What information should I include when prompting an AI assistant?
More detailed constraints equal better results—but only if it’s relevant. Keep your prompts clean and focused:
- Reference up-to-date documentation
- Add skills and rules files for assistants to use
- Link to coding guidelines when style matters
- Include TODO comments in code to steer Assistant’s implementation
Well-scoped context gives the assistant a better foundation to work from and results in more predictable outcomes.
3. Provide Examples to Guide Structure
Can AI assistants follow my code architecture?
Yes—if you show them what good looks like. AI is great at extrapolating from patterns:
- Give it a clean entity, and it can write a migration.
- Show it a well-structured controller, and it can build another.
You should implement core logic and architectural patterns yourself. Use AI to quickly scale those patterns across the rest of your codebase.
4. Stop Prompt Loops Early
What should I do if the AI assistant keeps giving bad output?
If your assistant starts looping, stop. Don’t keep asking it to fix its own broken suggestions.
Instead:
- Revert to the last working version
- Reframe your prompt with clearer direction
- Give the assistant more constraints by using TODOs, guidelines and conventions
- Restart the task from a clean slate
AI assistants have limited memory that can easily be polluted by repeatable runs. Start a new session for a new task.
5. Write the Critical Code Yourself
Should I rely on AI assistant for core logic?
No. You’re responsible for the outcome, not the assistant. Developers should:
- Own the architecture
- Write complex or sensitive logic themselves
- Use assistants to automate repetitive code and build well known structures, make core decisions yourself
AI is a tool—not a replacement for judgment.
6. Allow Assistants to validate their work
Can I make AI assistant even more productive?
If an assistant can validate freshly generated code, it can catch issues early and iterate.
- Let an assistant run linters and static analysis tools
- Include regression test suite and ensure an Assistant can run it
- Allow running your app in a sandbox (Yes - Assistants can use web browsers too!)
Tools that make developers productive makes AI productive too.
Summary: How to Work with Code Assistants Like a Pro
AI assistants won’t replace developers—but they will replace time wasted on boilerplate, scaffolding, and routine code tasks.
Here’s the playbook:
- Break problems into small, clear steps
- Give focused, relevant context
- Show good code examples to guide output
- Stop loops early—reset instead of repeating
- Own the logic that matters
To learn how today’s top tools compare—including GitHub Copilot, Cursor, Junie, and AWS Q—read AI: Help or Hype?
Want faster delivery without compromising quality? The best teams delegate with intent, not indifference.