My initial review of CoPilot and getting started is available here.
(What it does well – and why it’s not magic, but almost!)
Let me be clear: the Copilot Chat feature in VS Code can feel like a miracle until it’s not. When it’s working, you fire off a multi-line prompt defining what you want: “Build a function for X, validate Y, return Z…” and boom VS Code’s inline chat generates a draft that is scary good.
- What actually wins: It interprets your specification in context – your open file, project imports, naming conventions – and spits out runnable sample code. That’s not trivial; reputable models often lose context threading. Here, the chat lives in your editor, not detached, and that nuance matters.
It’s like sketching the spec in natural language, then having VS Code autocomplete not just code but entire behavior. - What you have to still do: Take a breath, a le sigh, and read it. Always. Control flow, edge cases, off-by-one errors Copilot doesn’t care. Security? Data leakage? All on you. Copilot doesn’t own the logic; it just stitches together patterns it’s seen. You own the correctness.
- Trick that matters: Iterate. Ask follow-up: “Okay, now handle invalid inputs by throwing
InvalidArgumentException,” or “Refactor this to async/await.” Having a chat continuum in the editor is powerful but don’t forget it’s your spec, not the AI’s.
Technique 2: Prompt With Skeleton First
Skip blindly describing behavior. Instead, scaffold it:
// Function: validateUserInput
// Takes { name: string, age: number }
// Returns { valid: boolean, errors: string[] }
// Edge cases: missing name, non-numeric age
function validateUserInput(input) {
// ...
}Then let Copilot fill in the body. Why this rocks:
- You’re giving structure; types, return shapes, edge conditions.
- The code auto-generated fits into your skeleton, adhering to your naming, your data model.
- You retain control over boundaries, types, and structure even before Copilot chimes in.
Downside? If your skeleton is misleading or incomplete, Copilot will “fill in” confidently, in code that compiles but does the wrong thing. Again, your code review has to rule.
Technique 3: In-Context Refactoring Conversations (AKA “Let me fix your mess, Copilot”)
Ever accepted a Copilot suggestion, then hated it? Instead of discarding, turn on Copilot Chat:
- Ask it: “Refactor this to reduce nesting and improve readability,” or “Convert this to use
.reduce()instead of.forEach().” - Watch it rewrite within the same context not tangential code thrown at you.
That’s one of its massive values – context-aware surgical refactoring – not blanket “clean this up” that ends in a different variable naming scheme or method order from your repo.
The catch: refactor prompts depend on Copilot’s parsing of your style. If your code is sloppy, it’s going to be sloppily refactored. So yes you still have to keep code clean, comment clearly, and limit complexity. Copilot is the editor version of duct tape not a refactor wizard.
The Brutal Truth
- VS Code + Copilot isn’t a magical co-developer. It’s a smart auto-completer with chat, living in your IDE, context-aware but utterly obedient to your prompts.
- The trick is not the AI it’s how you lead it. The better your spec, skeleton, or prompt, the better your code.
- Your style skeptical, questioning, pragmatic fits perfectly. You don’t let it ride; you interrogate. And that’s exactly how it should be.
TL;DR Summary
| Technique | What Works | What Fails Without You |
|---|---|---|
| Chat-first spec | Detailed natural-language spec → meaningful code | No spec clarity → garbage logic |
| Skeleton prompts | Provides structure, types, expectations | Bad skeleton = bad code, fast |
| In-editor refactoring chat | Context-preserving improvements | Messy code → messy refactor |
If you want more details on how you integrate Copilot into CI, or your personal prompt templates drop me the demand below, and I’ll tackle it head-on next time.