Why AI Coding Tools Get It Wrong— Understanding the Technical Limits

AI coding tools are powerful, but they make mistakes by design. Learn why Copilot, Claude Code, and Cursor fail — and how to avoid common pitfalls.

1. Introduction: It's Not Because AI Is “Dumb”

Claude Code, GitHub Copilot, Cursor.
These AI coding tools are incredibly powerful, but if you’ve used them for real work, you’ve probably hit some walls:

• The build doesn’t pass.
• Unit tests break.
• Sometimes, the AI even suggests code that could accidentally wipe your production database.

It’s easy to assume this happens because “AI isn’t smart enough yet.”
But the truth is more subtle:
these tools are designed within technical constraints that make mistakes inevitable.

In this post, we’ll walk through real examples — with code — to explain why AI coding tools make mistakes and what’s being done to improve them.

2. Three Common Failure Patterns

Failure 1: Missing Dependencies in Large Repositories

When you ask AI to “update this function,” it often misses a call site somewhere else, leading to broken tests.

# models.py
def update_user_profile(user_id, payload):
    # Existing implementation
    pass

# services.py
def process_user():
    update_user_profile(uid, payload)

# AI-generated version (misses a call site)
def update_user_profile(user_id, payload, is_admin=False):
    # New implementation
    pass

Result
The build passes, but running tests fails:
TypeError: missing 1 required positional argument: 'is_admin'.

Why it happens
AI tools can only "see" within their context window — a limited slice of code provided to the model.
For large repositories, IDEs or plugins pick a subset of “relevant” files to send to the AI.
But fully mapping every dependency is technically hard — missing a file or two is inevitable.

Failure 2: Suggesting Deprecated APIs
You’re on the latest library version, but the AI suggests using a deprecated API, causing runtime errors.

# In pandas 2.0+, append() has been removed
df = df.append(new_row, ignore_index=True)
# Runtime: AttributeError: 'DataFrame' object has no attribute 'append'

# Correct way:
df = pd.concat([df, new_row], ignore_index=True)

Why it happens
AI models are trained on a snapshot of knowledge from their last training cutoff.
Some tools integrate RAG (Retrieval-Augmented Generation) to pull the latest docs,
but the updates aren’t guaranteed to be perfect or always up-to-date.

Failure 3: Type Errors Due to Incorrect Inference
In TypeScript, you sometimes get code that looks correct but crashes at runtime.

interface User {
  name?: string;
}

function processUser(user: User) {
  // AI forgot optional chaining
  return user.name.toUpperCase();
  // Runtime: TypeError: Cannot read properties of undefined
}

// Correct way:
function processUser(user: User) {
  return user.name?.toUpperCase() ?? 'UNKNOWN';
}

Why it happens
AI doesn’t actually run a type checker.
It predicts the “most likely” code based on patterns it has seen,
which means it can miss subtle constraints like nullable or optional properties.

3. Why AI Gets It Wrong — The Technical Constraints

3.1 The Context Window Limit

• Claude 3.5 Sonnet: ~200K tokens
• GPT-4 Turbo: ~128K tokens Sounds huge, but even that isn’t enough to process every file in a 500+ file repository. IDEs try to guess which files matter most, but selection algorithms aren’t perfect.

3.2 Knowledge Freshness

• Claude 3.5 Sonnet was trained on data up to April 2024.
• Any breaking API changes after that? The model won’t know.
• As a result, AI often suggests “the most common but outdated patterns.” Some tools mitigate this with RAG — dynamically pulling docs from the web — but accuracy depends on search quality and update frequency.

3.3 Lack of Static Analysis and Execution
AI doesn’t execute the code it writes.
It works by predicting the next likely token, not by validating correctness.

• No TypeScript compiler or mypy integration by default.
• No runtime checks unless the IDE explicitly runs the code.
• No feedback loop unless you test it yourself. Result: code that looks right but breaks at runtime.

4. How Different Tools Handle This Problem

4.1 GitHub Copilot
- Approach: IDE integration with local file context.
- Strengths: Fast, smooth completions.
- Limitations: Often struggles with cross-file dependencies in large repos.

4.2 Cursor
- Approach: Full-repo indexing + RAG.
- Strengths: Better at understanding large codebases.
- Limitations: Index updates can lag, leading to outdated suggestions.

4.3 Claude Code
- Approach: Terminal-based file editing with explicit user control.
- Strengths: Transparent — you choose which files to expose.
- Limitations: Accuracy depends on you picking the right files.

5. The Road Ahead — Emerging Solutions

5.1 Sandbox Execution
- Idea: Run generated code in an isolated environment.
- Benefit: Move from guessing to verifying.
- Challenge: Security risks, slower feedback loops.

5.2 Static Analysis Integration
- Idea: Combine AI generation with TypeScript, ESLint, mypy, etc.
- Benefit: Catch type and syntax errors early.
- Status: Some IDEs are beginning to experiment with this.

5.3 Dynamic Knowledge Updates (RAG)
- Idea: Fetch the latest docs and Stack Overflow threads on the fly.
- Benefit: Stay aligned with API changes and evolving best practices.
- Challenge: Still dependent on search precision and doc quality.

6. Takeaways — Use AI Wisely, Don’t Trust Blindly

AI coding tools make mistakes not because they’re “dumb,” but because of hard technical limits.

Current limitations

• Context window size
• Training data freshness
• Lack of static analysis and runtime validation

Practical tips

• Always review and test AI-generated code.
• Apply big changes incrementally.
• Combine AI tools with type checkers and linters for safety.

7. What’s Next?

The next breakthroughs may come from:
- Larger context windows
- Faster real-time code execution
- Deeper integration with static analyzers

How do you see this evolving?
Do you want smarter reasoning, better execution checks, or real-time context integration?

Let’s discuss in the comments. 👇