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:

• Ensure strict grounding to indexed textbook content only

• Implement citation system that links to specific chapters/sections

• Configure failure mode for out-of-scope queries

• Optimize response time to meet <500ms target

• 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)
}