Google·Product Manager·Onsite - Product Sense / Strategy·Senior

Senior

May 2026

Summary

Google PM interview, product sense round. One question, pretty focused, but it cut right to the core of what makes AI products hard to get right.

Questions Asked (1)

Users are saying Gemini gives confident but incorrect answers. How would you approach fixing this?

Product Sense & IdeationProduct StrategyProduct Analytics & Metrics

Author's notes

I started with metrics, which felt right but also kind of obvious.

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AI HintsAI Generated

Suggested Approach

Start by framing the problem as a trade-off between factuality and creativity, then propose a structured approach that includes diagnosis, solution ideation, and measurement. Emphasize user trust and safety while balancing innovation, and suggest iterative improvements with clear success metrics.

Pro tip: Acknowledge that hallucinations are inherent to LLMs and that the goal is mitigation, not elimination. Show you understand Google's AI Principles and the importance of transparency with users.

1. Define and Diagnose the Problem

Clarify what 'confident but incorrect' means (hallucinations) and identify root causes such as training data gaps, model overconfidence, or lack of grounding. Use user reports and internal metrics to quantify the issue.

2. Prioritize Solutions Based on Impact and Feasibility

Brainstorm potential fixes like retrieval-augmented generation, uncertainty quantification, user feedback loops, and prompt engineering. Evaluate each on impact, effort, and alignment with Google's AI principles.

3. Design Experiments and Metrics

Propose A/B tests or pilot programs to measure improvements in factuality, user trust, and engagement. Define metrics such as hallucination rate, user-reported accuracy, and retention.

4. Implement and Iterate

Roll out successful solutions gradually, monitor metrics, and gather user feedback. Be prepared to pivot based on results and scale what works.

5. Communicate and Educate Users

Enhance transparency by labeling confidence levels or providing sources. Educate users on model limitations to set realistic expectations and maintain trust.

Key Points to Mention

Hallucination mitigation techniques like retrieval-augmented generation (RAG) and fine-tuning with factual data
Uncertainty estimation and confidence calibration to reduce overconfident wrong answers
User feedback mechanisms and human-in-the-loop evaluation
Metrics for measuring factuality and user trust (e.g., hallucination rate, user satisfaction)
Balancing safety, helpfulness, and creativity in line with Google's AI Principles
Transparency features like citations or confidence scores to help users judge responses

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.