---
title: Code Explanation
description: Use ControlFlow to generate natural language explanations of code snippets.
icon: code
---

This example demonstrates how to use ControlFlow to create a task that explains code snippets in natural language. It showcases the use of custom types and context passing for code documentation tasks.

## Code

The following code creates a function that takes a code snippet and its programming language, then returns an explanation of the code:

```python
import controlflow as cf
from pydantic import BaseModel

class CodeExplanation(BaseModel):
    code: str
    explanation: str
    language: str

def explain_code(code: str, language: str=None) -> CodeExplanation:
    return cf.run(
        f"Explain the following code snippet",
        result_type=CodeExplanation,
        context={"code": code, "language": language or 'auto-detect'}
    )
```

Now we can use this function to explain a code snippet:

<CodeGroup>
```python Example
code_snippet = """
def fibonacci(n):
    if n <= 1:
        return n
    else:
        return fibonacci(n-1) + fibonacci(n-2)
"""

result = explain_code(code_snippet, "Python")
print(f"Code:\n{result.code}\n")
print(f"Explanation:\n{result.explanation}")
```

```text Output
Code:
def fibonacci(n):
    if n <= 1:
        return n
    else:
        return fibonacci(n-1) + fibonacci(n-2)

Explanation:
This Python code defines a function called `fibonacci` that calculates 
the nth number in the Fibonacci sequence using recursion. Here's a 
breakdown of how it works:

1. The function takes a single parameter `n`, which represents the 
position in the Fibonacci sequence we want to calculate.

2. There's a base case: if `n` is less than or equal to 1, the function 
simply returns `n`. This handles the first two numbers in the Fibonacci 
sequence (F(0) = 0 and F(1) = 1).

3. For any other value of `n`, the function recursively calls itself twice:
   - Once with `n-1` as the argument
   - Once with `n-2` as the argument

4. The results of these two recursive calls are added together and returned.

This implementation follows the mathematical definition of the Fibonacci 
sequence, where each number is the sum of the two preceding ones. However, 
it's worth noting that this recursive approach can be inefficient for 
large values of `n` due to repeated calculations.
```
</CodeGroup>

## Key concepts

This implementation showcases several important ControlFlow features:

1. **[Pydantic models](/concepts/tasks/task-results#pydantic-models)**: We use a Pydantic model (`CodeExplanation`) to define the structure of our explanation result. This ensures that the task returns well-structured, consistent results including the original code, its explanation, and the programming language.

   ```python
   class CodeExplanation(BaseModel):
       code: str
       explanation: str
       language: str
   ```

2. **[Context passing](/concepts/tasks#context)**: We pass both the code snippet and the programming language as context to the task, providing all necessary information for the explanation process.

   ```python
   context={"code": code, "language": language}
   ```

By leveraging these ControlFlow features, we create an efficient and flexible code explanation tool. This example demonstrates how ControlFlow can be used to build AI-powered documentation workflows that can help developers understand and explain code snippets in natural language.