How do you programmatically measure Faithfulness, Answer Relevance, and Context Precision?

If a user asks a vague question like "Tell me more about that," how do you ensure the retriever finds relevant documents from past conversation history?

Explain the "Self-RAG" or "Corrective RAG" pattern. How does the model decide if it needs to go back and retrieve more data?

How would you architect a retrieval system to solve this 'Multi-hop' problem? Example question: How does the Q3 revenue of our Tokyo office compare to the bonus of the CEO?