By Arjun Mehta
GitHub Copilot is incredible at generating code. But it has a fundamental blind spot: it doesn't understand your codebase.
When you ask Copilot to generate a payment processing function, it generates valid Python code. But it doesn't know:
Where payment processing happens in your existing system. What services it integrates with. What patterns you've already established. What edge cases your system handles.
So it generates syntactically correct code that doesn't fit your system.
The Blindspot Explained
Copilot works on patterns from training data. It's excellent at:
Generating boilerplate code. Completing functions. Creating tests. Writing documentation.
It's terrible at:
Understanding your specific system. Following your patterns. Respecting your constraints. Knowing what you've already built.
The Result
Teams using Copilot see:
Higher code generation speed. Faster to ship. Lower quality code. Needs significant review. More code duplication. Copilot doesn't know about existing code. More inconsistent patterns. Copilot doesn't understand your patterns.
You're shipping faster but dirtier.
Why This Matters
Consistency across the codebase is how you reduce cognitive load. When code looks the same, it's easier to understand. When code uses the same patterns, it's easier to maintain.
Copilot breaks this. It generates new code in the style of generic Python, not the style of your system.
Reviewers have to correct every piece of generated code to match your patterns. The speed advantage evaporates.
Solving the Blindspot
Approach 1: Provide Context
Before generating code, tell Copilot about your system:
"Here's our payment service API." "Here's our error handling pattern." "Here's our naming convention."
With context, Copilot generates better code.
Approach 2: Use Local Models
Fine-tune models on your codebase:
GitHub Copilot for Business allows custom training. LocalGPT lets you run models on your code. Private models trained on your codebase understand your patterns.
Approach 3: Combine with Codebase Intelligence
Use tools that understand your code. Glue analyzes your codebase and provides context.
Flow: You ask Copilot to generate a feature. Copilot asks Glue: "What does this feature require?" Glue provides context (existing patterns, related services, constraints). Copilot generates code that fits your system.
This is the future: AI-assisted development that understands your codebase.
The Timeline
In 2024, Copilot is blind to your codebase.
In 2025, Copilot for Business adds codebase training.
In 2026, Copilot + codebase intelligence tools will be the norm. Code generation will be fast and accurate because the AI understands your system.
Teams that combine Copilot with codebase intelligence tools get the best results today.
Getting Started
- Use Copilot for boilerplate and tests, not core logic.
- Review generated code carefully for consistency.
- Add codebase context before generating code.
- Use tools like Glue to provide system understanding.
- As models improve, increase reliance on generated code.
Copilot isn't the enemy. Blindness to your codebase is. Solve that, and Copilot becomes powerful.
Frequently Asked Questions
Isn't Copilot still faster than manual coding? Faster to generate, not necessarily faster overall. Generated code needs heavy review. Manual code from experienced engineers often needs less review. Total time can be similar or slower.
Will better models solve this? Yes. Models fine-tuned on your codebase will generate code that fits your system. This is coming.