If you are one of the growing number of developers using AI coding tools, you might be wondering if you are making the most out of these powerful tools.
When I first started using GitHub Copilot, I expected it to be an instant game-changer. And it was – but just not in the way I thought. I'd get amazing results on some tasks and dead-ends on others. Then I realized the difference wasn't really about the tool. It was how I was using it.
Here are the tips that made the biggest difference.
Scope your context deliberately
Avoid dumping your whole project and asking a vague question. Attach only the specific file(s) or folder that's relevant. Tight context leads to faster, more accurate responses, and less wasted quota.
Plan mode before Agent mode
Before letting the agent touch your code, run it in Plan mode first. It maps out the approach, you catch misunderstandings early, and the actual execution is dramatically cleaner. This saves a lot of messy rollbacks.
Use @terminal instead of copy-pasting errors
You can give Copilot direct terminal access in Visual Studio Code with @terminal. This is much faster and more effective than manually copying and pasting error messages.
Ask for multiple options
Models often jump to the first solution they think of. Forcing options surfaces better architectural choices. For example, try asking: "Give 3 different approaches with trade-offs. Then recommend one."
Use cheaper models for throwaway tasks
For basic tasks like "Convert this curl to a Python dict", "explain this stack trace", or "write a one-liner regex", switch to using smaller models such as GPT-4o mini. Save premium models for complex tasks.
Pace your quota
If you're on a monthly cap, follow a simple rule: use <33% by day 10, and <66% by day 20. If you're blowing through your tokens in week one, you're probably being too lenient with your model selection.
The "Additional paid premium requests" option exists, but do treat it like a circuit breaker – something you flip only when you're about to run out of quota and have no other choice.
Split your copilot-instructions.md if it's getting long
After ~400-500 lines of file size, your instructions file starts hurting more than helping. Break it into focused files like auth_instructions.md, schema_instructions.md, etc. Even better, you can also ask Copilot to do the splitting for you.
Skip the pleasantries
No "thanks", "please", or "great job". Every token you send eats into the context the model uses for reasoning. Keep prompts dense and direct.
Reimagining developer workflows
These are the practices that work for me now, but I'm genuinely excited about how fast this is moving. New capabilities show up constantly, and there's always room to experiment and discover better approaches.
We're all figuring this out together, so test these ideas, and don't hesitate to replace them when something better comes along.
_HFpro5d6k3.png&w=256&q=75)
_E4PyVpfAxa.png&w=256&q=75)
-(1).png&w=1920&q=75)
-(1)_VZGHqxCgXV.png&w=1920&q=75)
