Technical documentation is essential but often neglected. AI assistance is changing the economics—generating first drafts, keeping docs synchronized with code, and answering documentation queries. The goal isn’t to replace technical writers but to reduce the friction of documentation.
Here’s how to use AI for technical documentation.
Documentation Challenges
Why Documentation Falls Behind
documentation_challenges:
creation:
- Takes time away from coding
- Requires different skills
- Not prioritized
- Hard to start from blank page
maintenance:
- Code changes, docs don't
- No automated sync
- Stale docs worse than none
- Who owns updates?
discovery:
- Docs exist but unfindable
- Search is poor
- Wrong information found
AI Documentation Tools
Code-to-Documentation
class DocGenerator:
"""Generate documentation from code."""
async def generate_function_doc(
self,
code: str,
style: str = "google"
) -> str:
return await self.llm.generate(
prompt=f"""Generate {style}-style documentation for this function.
Include:
- Brief description
- Args with types and descriptions
- Returns description
- Raises (if applicable)
- Example usage
Function:
```python
{code}
Documentation:""" )
async def generate_module_doc(
self,
module_code: str
) -> str:
return await self.llm.generate(
prompt=f"""Generate module-level documentation for this Python module.
Include:
- Module description
- Key classes/functions overview
- Usage examples
- Dependencies
Module:
{module_code}
Module documentation:""" )
async def generate_api_doc(
self,
endpoint_code: str
) -> str:
return await self.llm.generate(
prompt=f"""Generate API documentation for this endpoint.
Include:
- Endpoint description
- HTTP method and path
- Request parameters/body
- Response format
- Error codes
- Example request/response
Endpoint code:
{endpoint_code}
API documentation (OpenAPI-compatible):""" )
### Documentation Updater
```python
class DocUpdater:
"""Keep documentation synchronized with code."""
async def detect_staleness(
self,
code: str,
docs: str
) -> StalenessReport:
result = await self.llm.generate(
prompt=f"""Compare this code with its documentation.
Identify any inconsistencies or outdated information.
Code:
{code}
Documentation:
{docs}
List each inconsistency:
- What the docs say
- What the code does
- Severity (high/medium/low)
"""
)
return StalenessReport.parse(result)
async def update_docs(
self,
code: str,
current_docs: str
) -> str:
report = await self.detect_staleness(code, current_docs)
if not report.inconsistencies:
return current_docs
return await self.llm.generate(
prompt=f"""Update this documentation to match the code.
Current documentation:
{current_docs}
Issues found:
{report.format_issues()}
Updated code:
{code}
Write updated documentation that fixes these issues:"""
)
Documentation Q&A
class DocQA:
"""Answer questions using documentation."""
def __init__(self, vector_store, llm):
self.vector_store = vector_store
self.llm = llm
async def index_documentation(self, docs_path: str):
"""Index documentation for search."""
docs = await self._load_docs(docs_path)
for doc in docs:
chunks = self._chunk_document(doc)
embeddings = await self._embed(chunks)
for chunk, embedding in zip(chunks, embeddings):
await self.vector_store.insert(
text=chunk.text,
embedding=embedding,
metadata={
"source": doc.path,
"section": chunk.section
}
)
async def answer_question(
self,
question: str,
top_k: int = 5
) -> Answer:
# Search relevant docs
relevant_docs = await self.vector_store.search(
query=question,
top_k=top_k
)
# Generate answer
answer = await self.llm.generate(
prompt=f"""Answer this question using the documentation below.
Question: {question}
Relevant documentation:
{self._format_docs(relevant_docs)}
If the answer isn't in the documentation, say so.
Answer:"""
)
return Answer(
text=answer,
sources=[doc.metadata["source"] for doc in relevant_docs]
)
Workflow Integration
Pre-commit Documentation Check
# .pre-commit-config.yaml
repos:
- repo: local
hooks:
- id: doc-check
name: Documentation Check
entry: python scripts/check_docs.py
language: python
files: \.(py|ts|go)$
# scripts/check_docs.py
async def check_documentation(files: list[str]) -> CheckResult:
issues = []
for file_path in files:
code = read_file(file_path)
doc_path = get_doc_path(file_path)
if not exists(doc_path):
issues.append(Issue(
file=file_path,
type="missing_docs",
message="No documentation found"
))
continue
docs = read_file(doc_path)
staleness = await doc_updater.detect_staleness(code, docs)
if staleness.has_high_severity:
issues.append(Issue(
file=file_path,
type="stale_docs",
message=staleness.summary
))
return CheckResult(issues=issues, passed=len(issues) == 0)
CI Documentation Generation
# .github/workflows/docs.yml
name: Documentation
on:
push:
branches: [main]
jobs:
generate-docs:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Generate API Docs
run: python scripts/generate_api_docs.py
- name: Check for Changes
id: check
run: |
if git diff --quiet docs/; then
echo "changed=false" >> $GITHUB_OUTPUT
else
echo "changed=true" >> $GITHUB_OUTPUT
fi
- name: Create PR
if: steps.check.outputs.changed == 'true'
run: |
git checkout -b docs/auto-update
git add docs/
git commit -m "Auto-update documentation"
gh pr create --title "Update documentation" --body "Automated documentation update"
Best Practices
ai_documentation_practices:
generation:
- AI generates first draft
- Human reviews and edits
- Version control everything
- Track AI vs human contributions
maintenance:
- Automate staleness detection
- Update docs in same PR as code
- Regular sync jobs
quality:
- Define documentation standards
- Use consistent style
- Include examples
- Test code snippets
Key Takeaways
- AI reduces documentation friction significantly
- Generate first drafts, not final docs
- Automate staleness detection
- Keep docs close to code
- Use RAG for documentation Q&A
- Integrate into CI/CD workflow
- Human review remains essential
- Examples are more valuable than descriptions
- Track documentation coverage
- Treat docs like code—version and review
Documentation doesn’t have to lag behind. AI helps close the gap.