How can we explain the unexpected 50% increase in average response times for K Health's AI symptom checker in the last two weeks?

Introduction

The unexpected 50% increase in average response times for K Health's AI symptom checker over the past two weeks is a critical issue that demands immediate attention. This sudden performance degradation could significantly impact user experience, potentially leading to decreased engagement and trust in the platform. To address this problem, I'll employ a systematic approach to identify, validate, and resolve the root cause while considering both short-term fixes and long-term implications.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might have been a recent system update. Has there been any significant change to the AI model or infrastructure in the last month?

Why it matters: Recent changes often correlate with performance issues. Expected answer: Yes, there was a model update two weeks ago. Impact on approach: If confirmed, we'd focus on the update's impact and potential rollback.

• Considering user segments, I'm curious about the distribution of this increase. Is the 50% increase uniform across all user groups, or are certain segments more affected?

Why it matters: Helps identify if the issue is systemic or specific to certain user types. Expected answer: The increase is more pronounced in users with complex symptom inputs. Impact on approach: We'd investigate the AI's handling of complex queries and potential data biases.

• Given the nature of AI systems, I'm wondering about data quality. Have there been any changes in the data sources or input methods for the symptom checker recently?

Why it matters: Data quality directly impacts AI performance. Expected answer: No significant changes in data sources, but there's been an increase in user-generated content. Impact on approach: We'd examine the impact of increased user-generated content on system performance.

• Thinking about system load, has there been a significant increase in user traffic or query complexity in the last two weeks?

Why it matters: Increased load can strain system resources and impact response times. Expected answer: User traffic has remained stable, but query complexity has increased slightly. Impact on approach: We'd focus on optimizing the system's handling of complex queries.