LLM Output Drift Detector
What it is
A monitoring tool that detects silent behavioral changes in production LLM applications by running a fixed set of evaluation prompts on a schedule. It compares the embeddings of new responses against a stored baseline using cosine similarity and statistical metrics to alert users when semantic drift exceeds a threshold.
The problem it solves
LLM providers often update models without announcement, causing silent changes in tone, formatting, or refusal behavior that degrade application quality without changing the model version string.
What you could use it for
- Monitoring production LLM applications for silent provider updates
- Detecting semantic drift in model outputs over time
- Validating prompt stability across model versions
Read the source ↗Back to the library →
Summary written by an AI model from the source; check the source before you rely on it.