Skip to main content
Learn how teams evaluate AI code generation and explanation quality.

Overview

Goal: Ensure AI code assistants generate correct, readable, maintainable code. Time Investment: 30 minutes to first insights

The Challenge

You’re building a code assistant for your developers. Questions:
  • How correct should generated code be?
  • What code style matters?
  • How should explanations be written?
  • When should the AI say “I don’t know”?

Quick Example: Python Code Assistant

Step 1: Create Project

System Prompt:
You are a Python coding assistant.
Generate correct, readable Python code.
Explain code clearly using simple language.
Use modern Python practices (Python 3.10+).

Step 2: Add Scenarios

Write a function to check if a string is a palindrome
Create a decorator to log function calls
Implement binary search algorithm
Write a context manager for file handling
Explain what list comprehension does
Fix this code: [buggy code snippet]

Step 3: Generate, Rate, Extract

Rate based on:
  • Correctness: Does code work?
  • Style: Follows PEP 8?
  • Clarity: Easy to understand?
  • Comments: Well-explained?

Step 4: Get Insights

Patterns reveal:
  • All 5-star: Code has clear variable names
  • All 5-star: Includes example usage
  • Low-star: Missing error handling
  • Low-star: Overly complex when simpler solution exists

Scenarios by Language

Python

Write a function to [task]
Create a [data structure] implementation
Explain [Python concept]
Optimize this code for performance
Fix this error: [error message]

JavaScript

Write a React component that [requirement]
Create an async function to [task]
Explain how [JavaScript concept] works
Refactor this code for readability
Debug this issue: [description]

Go/Rust/etc.

Similar patterns for other languages.

Key Metrics for Code Quality

What Makes Code “5-Star”?

  • Correctness: Works as expected
  • Readability: Clear variable names, good structure
  • Efficiency: No unnecessary complexity
  • Safety: Handles edge cases and errors
  • Best Practices: Follows language conventions

Common Failure Patterns

Pattern 1: Incorrect Implementation
  • 5-star: Correct algorithm
  • 1-star: Off-by-one errors, wrong logic
Pattern 2: Poor Naming
  • 5-star: count_active_users()
  • 1-star: x = 5
Pattern 3: Missing Error Handling
  • 5-star: Handles edge cases, throws meaningful errors
  • 1-star: Assumes happy path, crashes on edge cases
Pattern 4: Poor Explanation
  • 5-star: Clear step-by-step explanation
  • 1-star: Assumes reader knows everything

Evaluation Tips

For Generated Code:
  • Test it (does it compile/run?)
  • Check correctness (does it solve the problem?)
  • Review readability (would you merge this?)
  • Consider efficiency (any obvious optimizations?)
For Explanations:
  • Is it accurate?
  • Is it at the right level of detail?
  • Would a junior dev understand it?
  • Does it include examples?

Iteration Example

Iteration 1 (60% success):
  • Issue: Code is correct but missing comments
  • Fix: Add “Include clear comments for complex logic”
Iteration 2 (75% success):
  • Issue: Doesn’t handle edge cases
  • Fix: Add “Include error handling and edge case checks”
Iteration 3 (88% success):
  • Issue: Explanations assume too much knowledge
  • Fix: Add “Explain assumptions and provide beginner-friendly context”
Final (94% success): Ready for production

Export for Engineering

  1. Export golden examples (5-star code snippets)
  2. Extract patterns (best practices discovered)
  3. Use in:
    • Code review guidelines
    • Junior dev training
    • CI/CD integration (syntax checking)
    • Documentation of expected behavior

Next Steps