Evaluating tools for detecting LLM model drift
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A developer community is discussing tools for detecting LLM model drift, following up on earlier inquiries. The conversation focuses on evaluating tools like PromptCanary and PromptLens, noting that others such as Libretto and Benchwright appear to have stalled. Key questions revolve around whether these tools can detect subtle quality drops versus just format breaks, their false-positive rates, and integration requirements, specifically if they need an SDK and production traffic or can directly hit prompts.
Following up on something I asked here a while back about catching LLM model drift. I've been looking at the actual tools now: PromptCanary, PromptLens, a couple others that seem to have stalled (Libretto, Benchwright).
Has anyone here actually run one in anger? Trying to understand: - does it catch subtle quality drops, or just format/schema breaks? - false-positive rate, does it become noise you mute? - does it need you to integrate an SDK + send production traffic, or can it just hit your prompts directly?