rag-chatbot-enhancement

majiayu000's avatarfrom majiayu000

Improves the RAG (Retrieval-Augmented Generation) chatbot for the Physical AI & Humanoid Robotics textbook with strict grounding, citation requirements, and performance optimization.

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[Tutoring and Explanations]

When & Why to Use This Skill

This Claude skill optimizes RAG (Retrieval-Augmented Generation) chatbots specifically for the 'Physical AI & Humanoid Robotics' textbook. It ensures high-fidelity educational support by implementing strict grounding, automated citation systems, and performance tuning to deliver accurate, hallucination-free academic responses in under 500ms.

Use Cases

  • Case 1: Academic Tutoring - Providing students with precise answers to complex robotics questions, backed by direct citations to specific textbook chapters and sections.
  • Case 2: Hallucination Prevention - Configuring the chatbot to strictly adhere to indexed textbook content and politely decline out-of-scope queries to maintain educational integrity.
  • Case 3: Low-Latency Learning - Optimizing vector database queries and response workflows to ensure students receive interactive, near-instant feedback during study sessions.
nameRAG Chatbot Enhancement
descriptionImproves the RAG (Retrieval-Augmented Generation) chatbot for the Physical AI & Humanoid Robotics textbook with strict grounding, citation requirements, and performance optimization.
when to useUse this skill when you need to enhance the chatbot's ability to answer questions based strictly on textbook content, provide citations, or improve response quality and performance.

Instructions: You are an expert in RAG systems and educational chatbots. Your task is to enhance the chatbot's ability to answer questions based strictly on the Physical AI & Humanoid Robotics textbook content, with proper citations and without hallucination.

Workflow:

  1. Ensure strict grounding to indexed textbook content only
  2. Implement citation system that links to specific chapters/sections
  3. Configure failure mode for out-of-scope queries
  4. Optimize response time to meet <500ms target
  5. Implement quality checks to prevent hallucination

Technical Requirements:

  • Use only indexed textbook content (no web search)
  • Include direct citations to source material
  • Return polite refusal for out-of-scope queries
  • Target <500ms response time for 95% of requests
  • Use Qdrant Cloud Free Tier for vector storage
  • Implement proper error handling and fallbacks

Output Format: Chatbot responses should include the answer, source citations, and appropriate error handling.

Example Use Case: User: "How does the chatbot handle queries outside the textbook content?"

Expected Output:

def handle_query(query: str) -> dict:
    # Search vector database for relevant textbook content
    results = qdrant_service.search(query)

    if not results:
        return {
            "answer": "I can only answer questions based on the content of the textbook. The requested information is not available in the indexed textbook materials.",
            "citations": [],
            "confidence": 0.0
        }

    # Verify content relevance and extract answer
    answer = generate_answer_from_context(results, query)

    # Format citations
    citations = [
        {
            "chapter": result.chapter,
            "section": result.section,
            "url": f"/docs/{result.chapter_slug}#{result.section_slug}"
        }
        for result in results
    ]

    return {
        "answer": answer,
        "citations": citations,
        "confidence": calculate_confidence(results)
    }
rag-chatbot-enhancement – AI Agent Skills | Claude Skills