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Introduction
ChatGPT's 30% increase in response time over the past week is a critical issue that demands immediate attention. As we analyze this performance degradation, we'll employ a systematic approach to identify, validate, and address the root cause while considering both short-term fixes and long-term implications for the product.
I'll begin by clarifying the context, then rule out external factors before diving deep into the product's user journey and metrics. We'll generate data-driven hypotheses, conduct root cause analysis, and develop a comprehensive plan to resolve the issue and prevent future occurrences.
Framework overview
This analysis follows a structured approach covering issue identification, hypothesis generation, validation, and solution development.
Step 1
Clarifying Questions (3 minutes)
Why it matters: Recent changes often correlate with performance shifts. Expected answer: Yes, a model update or infrastructure change. Impact on approach: If yes, we'd focus on rollback options and change management processes.
Why it matters: User feedback can validate our metrics and highlight severity. Expected answer: An uptick in user complaints. Impact on approach: If yes, we'd prioritize immediate user-facing solutions.
Why it matters: Ensures we're addressing a real issue, not a measurement error. Expected answer: No changes in measurement methodology. Impact on approach: If there were changes, we'd need to re-evaluate our baseline metrics.
Why it matters: Unusual demand could explain performance issues. Expected answer: Possible increase in complex queries or user volume. Impact on approach: If true, we'd focus on scaling solutions and query optimization.
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