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Design a grounded voice assistant

AppleMachine Learning Engineer · Onsite
MediumMachine Learning EngineerOnsiteML System Design

Last updated

This problem evaluates your ability to design a retrieval-augmented generation (RAG) system for a voice assistant, focusing on grounding model outputs in trusted sources to prevent hallucinations.

Problem Statement

You are tasked with designing the architecture for a grounded voice assistant. The system must answer user queries by retrieving information from a large, trusted internal knowledge base (e.g., product manuals, help articles) rather than relying solely on the LLM's parametric memory. The goal is to maximize response accuracy and grounding while maintaining low latency suitable for real-time voice interaction.

Requirements

  1. Functional Requirements:
    • Ability to handle multi-turn conversations where the user refers to previous context.
    • Integration of a retrieval mechanism to fetch relevant documents.
    • A verification layer to ensure the generated answer is supported by retrieved content.
  2. Non-functional Requirements:
    • Latency: Must start streaming the response within 500ms.
    • Safety: Minimize hallucinations; if the system does not have sufficient information, it should explicitly state that it does not know.
  3. Scale Assumptions:
    • Knowledge base: 1 million documents.
    • Query volume: 10,000 queries per second (QPS).

Your Task

Please provide a high-level design that covers:

  • The retrieval pipeline (e.g., embedding model, vector store selection, and reranking strategy).
  • The LLM integration (how you feed the context to the model to enforce grounding).
  • The evaluation strategy: How do you measure 'groundedness' and hallucination rates in a production environment?
  • Trade-offs between retrieval depth (latency) vs. answer quality.

Hints

Hint 1

Consider using a hybrid approach combining sparse (keyword-based) and dense (vector-based) retrieval for better accuracy.

Hint 2

Think about 'Chain of Verification' or 'Self-Correction' loops where the model checks its own generated response against the retrieved source snippets.

Hint 3

To handle latency, evaluate the trade-offs between using a smaller, distilled LLM for generation versus a larger model, and how caching can improve performance.

Follow-up questions

  • How would you handle a situation where the retrieved documents contain conflicting information?
  • If the voice assistant is deployed on a mobile device, how would you decide which components run on-device versus in the cloud?
  • What metrics would you use to measure the trade-off between user satisfaction and system latency in a live A/B test?