Building independent LLM drift detection - sharing the methodology, looking for feedback on the approach
The author of Tickerr.ai shares their methodology for detecting LLM drift and seeks feedback on their approach. They monitor latency, TTFT, uptime, and error rates across major models. The goal is to identify silent model behavior drift, which is harder to detect than transport-level issues like latency or errors. Builders can use this approach to improve their own LLM monitoring.
Key takeaways
- Detecting silent LLM drift is harder than transport-level issues.
- Tickerr.ai monitors latency, TTFT, uptime, and error rates.
- Seeking feedback on drift detection methodology.
Building independent LLM drift detection - sharing the methodology, looking for feedback on the approach
The author of Tickerr.ai shares their methodology for detecting LLM drift and seeks feedback on their approach. They monitor latency, TTFT, uptime, and error rates across major models. The goal is to identify silent model behavior drift, which is harder to detect than transport-level issues like latency or errors. Builders can use this approach to improve their own LLM monitoring.
Key takeaways
- Detecting silent LLM drift is harder than transport-level issues.
- Tickerr.ai monitors latency, TTFT, uptime, and error rates.
- Seeking feedback on drift detection methodology.