Why GitHub Copilot Creates Stubs Instead of Working Code?

1. Designed to Assist, Not Auto-Complete

GitHub Copilot’s core design goal is inline assistance, not end-to-end generation. It predicts the next few logical tokens based on your file context and recent edits. When you write def fetch_data():, it assumes you’ll drive the implementation and only suggests the skeleton—unless your prompt makes it crystal clear you expect full implementation.

Think of it as “continuation prediction,” not “instruction fulfillment.”

2. Limited Context Window

Unlike ChatGPT or Cursor‘s agent mode, Copilot inside VS Code has:

• Limited context length (≈1,500–4,000 tokens)
• No memory of your prior files
• No understanding of your project goals or documentation

Without the full picture, it defaults to safe stubs—class shells, TODOs, or comments.

3. Different Engine Than ChatGPT

Even though both are OpenAI-powered, Copilot uses a specialized Codex-tuned model (or GPT-4-Turbo for Copilot X) optimized for low-latency completions, not reasoning-heavy generations.

Copilot writes:

def fetch_data(url):
    # TODO: implement data fetching
    pass

ChatGPT writes:

def fetch_data(url):
    response = requests.get(url)
    response.raise_for_status()
    return response.json()

4. Prompt Ambiguity Matters

Copilot doesn’t “see” intent like ChatGPT. If your comment says:

# Fetch API data and process it

It might not generate working code. But if you specify:

# Fetch API data using requests, parse JSON, and return the title field

The more specific the inline comment or variable name, the more complete the generation.

Even in Agent Mode (the conversational AI built into VS Code), Copilot remains cautious by design:

1. Built for Safety Over Speed

Copilot Agent doesn’t directly run or replace code—it’s built to suggest or scaffold. This is intentional to:

• Avoid generating code that could break your project or violate licenses
• Force you to confirm logic before committing
• Keep context under your control rather than silently overwriting

2. Model Restrictions

Copilot Agent doesn’t use the same models as ChatGPT Plus or Cursor. It runs a smaller, instruction-tuned OpenAI model optimized for fast in-editor responses—not deep reasoning.

That’s why Cursor or Claude often gives you full working code, while Copilot Agent “suggests” skeletons.

3. Vague Prompts = Stub Code

Vague prompt: “Add code to generate HTML for the AI output”

Result: A stub function

Better prompt: “Write the full function, not a stub or TODO. Implement complete working PHP logic using wp_kses_post(). Do not leave any placeholder.”

Result: Complete implementation

1. Anthropic Trusts Developers More

Claude doesn’t baby-proof its output. If you say “write a production-ready PHP class that connects to OpenAI and parses HTML responses,” it gives you the whole class. No TODOs, no empty functions—just code.

They assume you know what you’re doing and can review the result.

2. Better Architectural Understanding

In Claude Code (Anthropic’s native experience), you get large context and explicit control over extended thinking, while GitHub Copilot provides a managed, IDE-first product with smaller context and fewer knobs.

Cursor’s agent mode (built on Claude 3.5 Sonnet) reads your repo, infers dependencies, and writes actual code. GitHub notes that Claude Sonnet 4 excels in agentic scenarios and follows complex instructions with clear reasoning.

3. Policy vs. Performance

OpenAI’s cautious approach is partly policy, partly product design:

• Microsoft doesn’t want Copilot overwriting your codebase with errors or copyrighted material
• GitHub wants Copilot to act more like a pair programmer than an autonomous engineer
• OpenAI’s model alignment defaults to “be safe, not bold”—even when developers clearly know what they’re doing