Code Explanation
Explain complex code snippets
Overview
Turn complex code into clear explanations for code reviews, onboarding junior developers, and technical documentation. Generate line-by-line breakdowns with time/space complexity analysis in under 60 seconds for Python, JavaScript, TypeScript, Java, Swift, and more.
Use Cases
- Onboard junior developers with algorithm explanations during sprint planning
- Document legacy code that lacks comments before refactoring
- Generate technical explanations for code review pull requests
- Create training materials for team knowledge sharing sessions
- Explain open-source library internals when integrating third-party code
Benefits
- Generate detailed code breakdowns in under 60 seconds instead of writing explanations manually
- Maintain consistent documentation quality across distributed engineering teams
- Save 2+ hours per week on code review comments and PR feedback
- Accelerate junior developer onboarding by providing instant algorithm explanations
- Identify performance bottlenecks and optimization opportunities through complexity analysis
Template
Explain the following code:
Language: {{language}}
Code:
{{code}}
Target audience: {{audience}}
Include:
- High-level overview
- Line-by-line breakdown
- Key concepts used
- Time/space complexity
- Potential improvements
- Related patterns
Detail level: {{detailLevel}}
Properties
- language: Single Selection (default:
Python)- Options: Python, JavaScript, TypeScript, Java, Swift, and 5 more
- code: Multi-line Text
- audience: Single Selection (default:
Intermediate)- Options: Beginners, Intermediate, Advanced, Non-technical
- detailLevel: Single Selection (default:
Detailed)- Options: Brief, Detailed, Comprehensive
Example Output
Using this Python memoized Fibonacci implementation:
def fibonacci(n, memo={}):
if n in memo:
return memo[n]
if n <= 1:
return n
memo[n] = fibonacci(n-1, memo) + fibonacci(n-2, memo)
return memo[n]
The template generates:
High-level overview
Recursive Fibonacci implementation using memoization to cache computed values, avoiding redundant calculations. Returns the nth Fibonacci number.
Line-by-line breakdown
Line 1: Function signature with default mutable dictionary memo for caching
Line 2-3: Cache hit - return precomputed value if exists
Line 4-5: Base cases - F(0) = 0, F(1) = 1
Line 6: Compute F(n) = F(n-1) + F(n-2), store in cache
Line 7: Return cached result
Key concepts
Memoization (dynamic programming technique storing results to avoid recomputation), recursion (function calls itself with smaller inputs), and default mutable argument (dictionary persists across function calls).
Time/space complexity
Time: O(n) - each value computed once Space: O(n) - memo dictionary and call stack depth
Potential improvements
Critical issue: Default mutable argument creates shared state across calls. Use memo=None pattern instead. Iterative approach provides better space complexity O(1) for call stack.
Related patterns
Bottom-up dynamic programming, matrix exponentiation for O(log n) solution, tabulation instead of recursive memoization.
Common Mistakes to Avoid
Skipping complexity analysis for production code - Always include time/space complexity for algorithms that process user data or run in loops. Performance issues often hide in O(n²) operations that look innocent during development.
Explaining what instead of why - Junior developers need to understand why you chose recursion over iteration, not just what the code does. Focus on design decisions and tradeoffs.
Using wrong technical depth for your audience - Non-technical stakeholders don’t need Big O notation. Beginners need more context on basic concepts. Advanced developers want optimization details and edge cases.
Forgetting to mention gotchas - Call out language-specific quirks like Python’s mutable default arguments, JavaScript’s variable hoisting, or Swift’s copy-on-write behavior. These trip up developers switching languages.
Omitting practical alternatives - Show iterative solutions alongside recursive ones. Mention standard library functions that solve the problem. Real codebases favor readability over clever implementations.
Frequently Used With
- API Documentation - Document code examples in API endpoint guides
- Code Review - Explain code sections flagged during review
- Refactoring Plan - Document existing code structure before refactoring
Get Early Access to Migi
Stop context-switching between ChatGPT and your codebase. Get early access to Migi and turn code documentation into a saved workflow with team templates, keyboard shortcuts, and instant explanations.