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Developer proposes LLM drift detection service for silent performance degradation

A developer is proposing a new service to detect silent degradation in large language model (LLM) performance, beyond standard API health checks. The proposed tool would run external canary tests to monitor aspects like JSON adherence, instruction following, and refusal behavior, comparing current model outputs against historical baselines and peer models. The developer is seeking feedback on the technical soundness, valuable alert types, and potential pricing for such a service, particularly for agentic systems where subtle performance shifts can lead to significant operational failures. AI

IMPACT Could improve reliability and trust in LLM deployments, especially for agentic systems.

RANK_REASON Developer proposes a new tool/service for LLM monitoring.

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COVERAGE [1]

  1. r/OpenAI TIER_2 English(EN) · /u/Remarkable_Divide755 ·

    Building independent LLM drift detection - sharing the methodology, looking for feedback on the approach

    <!-- SC_OFF --><div class="md"><p>Disclosed upfront: I run [Tickerr dot ai], an independent external monitor for AI APIs. Today it tracks latency, TTFT, uptime, and error rates across major models.</p> <p>I’m trying to validate a more specific idea before building too much.</p> <…