Design a chatbot over structured and unstructured data

Q: Design a chatbot over structured and unstructured data

This question evaluates a machine learning engineer's ability to design end-to-end systems that integrate structured and unstructured data, testing competencies in data architecture, information retrieval, LLM orchestration, access control, privacy, and monitoring within the ML system design domain.

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Question

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Design a chatbot that can answer user questions using both:

Structured data (e.g., relational tables such as orders, products, pricing, user accounts), and
Unstructured data (e.g., documents, knowledge base articles, FAQs, PDFs, internal wikis).

The chatbot should:

Provide accurate, grounded answers
Support follow-up questions (multi-turn)
Cite sources when possible
Respect permissions (users can only access authorized data)

Discuss:

High-level architecture and main components
Data ingestion and indexing strategy for both data types
Retrieval strategy (including when to query SQL vs retrieve documents)
LLM prompting / tool-calling approach
Safety, privacy, and access control
Evaluation (offline + online) and monitoring
Latency/cost considerations and scalability

Design a chatbot over structured and unstructured data

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