GitHub Copilot: The First Impressions of AI Pair Programming

I’ve spent the last week working with the GitHub Copilot technical preview, and I can say without hyperbole that this is the most significant developer tooling shift since IntelliSense. But it requires a fundamental shift in how we approach coding.

It’s Not Just Autocomplete

Calling Copilot “autocomplete” is like calling a Ferrari a “faster horse.” It doesn’t just complete the current line; it completes the thought. When I type a comment describing a complex algorithm, Copilot often generates the entire implementation, including edge case handling.

For example, I needed a function to validate a JWT token and extract specific claims without using an external library (for a lightweight edge function). I typed:

// Function to decode JWT token without verifying signature
// Returns the payload as an object
function decodeJwt(token) {

Copilot immediately filled in the base64 decoding logic, correctly handling the URL-safe characters replacement, and parsing the JSON. It saved me 15 minutes of looking up the base64url spec.

The Danger Zone

However, it introduces a new risk: Illusion of Correctness. The code looks correct. It uses the right variable names, the right style, and compiles. But it might have subtle logical flaws or security vulnerabilities.

As senior engineers, our job shifts from “writing code” to “reviewing code.” You must review Copilot’s output closer than you review a junior developer’s PR, because Copilot has infinite confidence but zero understanding.

Best Practices for AI-Assisted Dev

• Context is King: Keep related files open. Copilot reads your open tabs to understand the context of your application.
• Descriptive Comments: Write the JSDoc/XML summary before the method. It guides the AI significantly better than the method name alone.
• Test First: Write your unit tests first. Let Copilot generate the implementation to pass the tests. This TDD loop is incredibly fast with AI.