Design a grounded voice assistant
Company: Apple
Role: Machine Learning Engineer
Category: ML System Design
Difficulty: medium
Interview Round: Onsite
You are designing a voice assistant response system similar to Siri. The assistant uses a large language model together with external tools or APIs to answer user requests. Discuss how you would design and evaluate this system.
Address the following:
1. How would you evaluate the overall quality of generated responses?
2. How would you ensure the final answer is grounded in the tool output rather than invented by the model?
3. Why do large language models hallucinate, and how would you reduce or handle hallucinations in production?
4. If the available context becomes too long, such as long conversation history and user profile data, how would you manage context efficiently while preserving answer quality?
Assume this is a production consumer assistant, so accuracy, latency, safety, and user experience all matter.
Quick Answer: This question evaluates understanding of grounding strategies for large language models, causes and mitigation of hallucinations, response quality evaluation metrics, context management for long histories, and trade-offs among accuracy, latency, safety, and user experience in a production voice assistant within the ML System Design domain for a Machine Learning Engineer role. It is commonly asked to assess system-level design and practical application skills in building reliable, safe, and scalable conversational AI, requiring both conceptual understanding and practical implementation considerations.