Building AI-Powered Documentation Systems

documentation_problems:
  discovery:
    - Search returns irrelevant results
    - Users don't know what to search for
    - Information scattered across pages

  comprehension:
    - Too technical or too basic
    - Missing context
    - Outdated examples

  maintenance:
    - Content becomes stale
    - No feedback loop
    - Low priority

  ai_solutions:
    - Semantic search and Q&A
    - Adaptive explanations
    - Automated freshness checks
    - Usage analytics for improvement

class AIDocumentationSystem:
    """AI-powered documentation system."""

    def __init__(
        self,
        content_store: ContentStore,
        vector_store: VectorStore,
        llm: LLM
    ):
        self.content = content_store
        self.vectors = vector_store
        self.llm = llm

    async def answer_question(
        self,
        question: str,
        context: UserContext
    ) -> DocAnswer:
        # Retrieve relevant documentation
        relevant_docs = await self._retrieve(question)

        # Generate answer
        answer = await self.llm.generate(
            system=self._doc_system_prompt(context),
            messages=[{
                "role": "user",
                "content": f"""Based on the documentation below, answer this question.
If the answer isn't in the docs, say so.

Documentation:
{self._format_docs(relevant_docs)}

Question: {question}

Answer:"""
            }]
        )

        return DocAnswer(
            answer=answer,
            sources=relevant_docs,
            confidence=await self._assess_confidence(answer, relevant_docs)
        )

    async def _retrieve(self, question: str) -> list[Document]:
        # Hybrid search
        semantic_results = await self.vectors.search(
            query=question,
            top_k=5
        )

        keyword_results = await self.content.keyword_search(
            query=question,
            top_k=5
        )

        # Combine and rerank
        combined = self._merge_results(semantic_results, keyword_results)
        reranked = await self._rerank(question, combined)

        return reranked[:5]

class ConversationalDocs:
    """Multi-turn documentation assistance."""

    async def chat(
        self,
        message: str,
        conversation: list[Message],
        session: DocSession
    ) -> DocResponse:
        # Build context from conversation
        conversation_context = self._extract_context(conversation)

        # Determine intent
        intent = await self._classify_intent(message, conversation_context)

        if intent == "question":
            return await self._answer_question(message, session)
        elif intent == "clarification":
            return await self._clarify_previous(message, conversation)
        elif intent == "example_request":
            return await self._provide_example(message, session)
        elif intent == "navigation":
            return await self._navigate_docs(message)

    async def _provide_example(
        self,
        request: str,
        session: DocSession
    ) -> DocResponse:
        # Get topic from request
        topic = await self._extract_topic(request)

        # Find or generate example
        existing_example = await self.content.find_example(topic)

        if existing_example:
            return DocResponse(
                content=existing_example,
                type="example"
            )

        # Generate example based on docs
        docs = await self._retrieve(topic)
        generated = await self._generate_example(topic, docs)

        return DocResponse(
            content=generated,
            type="generated_example",
            disclaimer="Generated example - verify before using"
        )

adaptive_explanations:
  expertise_detection:
    - Analyze question complexity
    - Track user history
    - Infer from role/context

  explanation_levels:
    beginner:
      - More context and background
      - Simpler language
      - Step-by-step guidance

    intermediate:
      - Standard documentation
      - Some assumed knowledge
      - Practical focus

    expert:
      - Technical depth
      - Edge cases
      - Advanced patterns

class FreshnessMonitor:
    """Monitor documentation freshness."""

    async def check_freshness(self, doc: Document) -> FreshnessReport:
        checks = await asyncio.gather(
            self._check_code_references(doc),
            self._check_api_versions(doc),
            self._check_link_validity(doc),
            self._compare_to_source(doc)
        )

        issues = [c for c in checks if c.stale]

        return FreshnessReport(
            document=doc.id,
            is_fresh=len(issues) == 0,
            issues=issues,
            last_checked=datetime.utcnow()
        )

    async def _check_code_references(self, doc: Document) -> CheckResult:
        code_blocks = self._extract_code_blocks(doc)

        for block in code_blocks:
            # Check if code still valid
            if block.language == "python":
                valid = await self._validate_python(block.code)
                if not valid:
                    return CheckResult(
                        stale=True,
                        reason=f"Code block may be outdated"
                    )

        return CheckResult(stale=False)