Why has the average response time for EliseAI's automated email classification system increased by 2 seconds in the past 48 hours?

Introduction

The recent increase in average response time for EliseAI's automated email classification system is a critical issue that demands immediate attention. This 2-second delay over the past 48 hours could significantly impact user experience and overall system efficiency. I'll approach this problem systematically, focusing on identifying the root cause, validating hypotheses, and developing both short-term fixes and long-term solutions.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking this could be related to a recent system update. Has there been any deployment or configuration change in the past week?

Why it matters: Recent changes often correlate with performance issues. Expected answer: Yes, a minor update was deployed 3 days ago. Impact on approach: If confirmed, we'd focus on the update's components and rollback options.

• Considering the specificity of the 2-second increase, I'm curious about our monitoring granularity. Are we seeing a consistent 2-second increase across all emails, or is this an average masking larger variations?

Why it matters: Understanding the distribution helps pinpoint if it's a systemic issue or affecting specific email types. Expected answer: It's an average, with some emails showing larger increases. Impact on approach: We'd segment the data to identify patterns in heavily impacted emails.

• Given the critical nature of email classification, I'm wondering about the current system load. Has there been any unusual spike in email volume or complexity recently?

Why it matters: Increased load could explain performance degradation. Expected answer: Email volume has been consistent, but complexity has increased slightly. Impact on approach: We'd investigate if the classification algorithm is struggling with more complex emails.

• Thinking about the AI model itself, I'm curious about its training cycle. When was the last time the model was retrained or fine-tuned?

Why it matters: Outdated models might struggle with evolving email patterns. Expected answer: The model was last retrained 3 months ago. Impact on approach: We'd consider if the model needs retraining or if there's drift in email characteristics.