What caused the sudden spike in API errors for Inflection AI's language model service last week?

Introduction

The sudden spike in API errors for Inflection AI's language model service last week is a critical issue that demands immediate attention and thorough analysis. As we delve into this problem, 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 our service.

Our analysis will follow a structured framework, beginning with clarifying questions to establish context, ruling out external factors, understanding the product and user journey, breaking down the relevant metrics, gathering and prioritizing data, forming hypotheses, conducting root cause analysis, and finally proposing validation methods and next steps.

Step 1

Clarifying Questions (3 minutes)

• I'm noticing the term "sudden spike" in the question. Would you say this increase in API errors occurred within a matter of hours or days?

Why it matters: The timeframe helps determine if this is an acute technical issue or a gradual degradation. Expected answer: Within 24 hours. Impact on approach: A rapid onset would suggest a more immediate technical cause rather than a gradual user behavior shift.

• Given that we're dealing with a language model service, I'm curious about the nature of these errors. Are we seeing a particular type of API error dominating the spike, such as timeouts or invalid responses?

Why it matters: Different error types point to different potential root causes. Expected answer: Primarily timeout errors. Impact on approach: Timeout errors might indicate capacity issues or backend processing problems.

• Considering the potential impact on users, can you share if this spike affected all users equally or if it was concentrated in specific user segments or geographic regions?

Why it matters: This helps determine if the issue is systemic or localized. Expected answer: The spike affected users globally but was more pronounced in certain regions. Impact on approach: Regional variation could suggest infrastructure or CDN-related issues.

• I'm wondering about any recent changes to the system. Were there any significant updates, deployments, or infrastructure changes in the days leading up to this spike?

Why it matters: Recent changes are often correlated with sudden performance issues. Expected answer: A minor update was pushed to production two days prior. Impact on approach: This could narrow our focus to recent changes as a potential trigger.

• Lastly, has there been any unusual pattern in usage or demand for the API service in the period leading up to or during the spike?

Why it matters: Unusual demand could strain system resources and lead to errors. Expected answer: There was a 20% increase in API calls in the 12 hours before the spike. Impact on approach: This might indicate a capacity issue or a potential DDoS attack.