How I Built an AI-Powered Documentation Generator That Costs $2/month to Run
Your codebase is growing. Your documentation is not. Every sprint, you promise to update the docs. Every sprint, you ship features instead. By month three, your README is a graveyard of outdated function signatures and deprecated API endpoints.
I hit that wall last quarter. My Python SDK had 47 public methods. The docs covered 12. I started manually writing docstring updates and realized I was burning 6 hours per week on busywork that an AI could handle in seconds.
So I built an automated documentation generator that parses my entire codebase, extracts function signatures and context, and generates accurate, formatted documentation using Claude with prompt caching. The kicker? It costs me $2 per month to run.
Here's exactly how it works, what I learned, and the code you can steal today.
The Problem With Existing Solutions
Most documentation tools fall into two camps: bloated SaaS platforms charging $99/month for features you don't need, or open-source generators that produce garbage output requiring manual cleanup.
The real issue isn't the tool. It's the economics of API calls.
Without optimization, generating docs for a medium-sized codebase (500+ functions) using GPT-4 costs $15-40 per run. Do that weekly? You're looking at $60-160/month just for API fees. That's before infrastructure.
The breakthrough came when I combined three techniques:
- Prompt caching — Claude caches your system prompts and context. Reuse them across calls and pay 90% less.
- Batch processing — Group function parsing into single API calls instead of one-per-function.
- Smart filtering — Only document public APIs, skip internal helpers.
The result: one full documentation generation for my entire codebase runs for $0.15.
Architecture Overview
Here's the flow:
Codebase → Parse Functions → Batch into Chunks →
Claude (with cached prompt) → Format Output → Deploy
I built this as a CLI tool that:
- Recursively scans a Python codebase
- Extracts function signatures, docstrings, and type hints
- Groups functions into batches (I use 10 functions per batch)
- Sends batches to Claude with a cached system prompt
- Generates markdown documentation
- Deploys output to a static docs site
Infrastructure: I deployed this on DigitalOcean's App Platform — setup took under 5 minutes and costs $5/month. The script itself runs on a $0 GitHub Actions schedule (free tier is generous).
Step 1: Parse Your Codebase
Start with a function that walks your repo and extracts metadata:
import ast
import os
from typing import List, Dict, Any
from dataclasses import dataclass
@dataclass
class FunctionMetadata:
name: str
module: str
signature: str
docstring: str
returns: str
args: List[Dict[str, str]]
def extract_functions(repo_path: str) -> List[FunctionMetadata]:
"""Walk codebase and extract all public functions."""
functions = []
for root, dirs, files in os.walk(repo_path):
# Skip common non-code directories
dirs[:] = [d for d in dirs if d not in {'.git', '__pycache__', '.venv', 'node_modules'}]
for file in files:
if not file.endswith('.py'):
continue
filepath = os.path.join(root, file)
try:
with open(filepath, 'r', encoding='utf-8') as f:
tree = ast.parse(f.read())
except SyntaxError:
continue
module_name = filepath.replace(repo_path, '').replace('/', '.').replace('.py', '')
for node in ast.walk(tree):
if isinstance(node, ast.FunctionDef):
# Skip private functions (start with _)
if node.name.startswith('_'):
continue
# Extract signature
args_list = []
for arg in node.args.args:
arg_name = arg.arg
arg_type = ast.unparse(arg.annotation) if arg.annotation else 'Any'
args_list.append({'name': arg_name, 'type': arg_type})
return_type = 'Any'
if node.returns:
return_type = ast.unparse(node.returns)
docstring = ast.get_docstring(node) or ''
signature = f"def {node.name}({', '.join([f'{a['name']}: {a['type']}' for a in args_list])}) -> {return_type}"
functions.append(FunctionMetadata(
name=node.name,
module=module_name,
signature=signature,
docstring=docstring,
returns=return_type,
args=args_list
))
return functions
Run this on your repo and you'll get structured data for every public function. For my SDK with 47 functions, this took 200ms.
Step 2: Batch Functions for API Efficiency
Grouping functions into batches dramatically reduces API overhead:
from typing import List
def batch_functions(functions: List[FunctionMetadata], batch_size: int = 10) -> List[List[FunctionMetadata]]:
"""Group functions into batches."""
return [functions[i:i + batch_size] for i in range(0, len(functions), batch_size)]
def format_batch_for_prompt(batch: List[FunctionMetadata]) -> str:
"""Format a batch of functions as context for Claude."""
formatted = "# Functions to Document:\n\n"
for func in batch:
formatted += f"## {func.module}.{func.name}\n"
formatted += f"**Signature:**\n```
{% endraw %}
python\n{func.signature}\n
{% raw %}
```\n"
if func.docstring:
formatted += f"**Existing Docstring:**\n{func.docstring}\n"
formatted += "\n"
return formatted
This converts a batch of 10 functions into a single prompt. Much cheaper than 10 separate API calls.
Step 3: Call Claude With Prompt Caching
This is where the magic happens. Prompt caching lets you reuse expensive context across multiple API calls:
python
import anthropic
import json
client = anthropic.Anthropic(api_key="your-api-key")
# This system prompt gets cached — you pay for it once, use it many times
SYSTEM_PROMPT = """You are an expert technical writer. Generate clear, concise documentation for Python functions.
For each function, create a markdown section with:
1. **Description**: 1-2 sentences explaining what it does
2. **Parameters**: Table of parameter names, types, and descriptions
3. **Returns**: Description of return value
4. **Example**: A practical code example showing usage
5. **Raises**: Any exceptions that might be raised
Use this format:
### function_name
[Description]
**Parameters:**
| Name | Type | Description |
|------|------|-------------|
| param1 | str | What it does |
**Returns:** [description]
**Example:**
\`\`\`python
[example code]
\`\`\`
**Raises:** [exceptions]
Be concise but complete. Assume the reader is a developer using this API."""
def generate_docs_for_batch(batch: List[FunctionMetadata]) -> str:
"""Call Claude to generate docs for a batch of functions."""
batch_text = format_batch_for_prompt(batch)
response = client.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=2000,
system=[
{
"type": "text",
"text": SYSTEM_PROMPT,
"cache_control": {"type": "ephemeral"} # This enables caching
}
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