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LLM monitoring

Large language model monitoring that does away with ambiguity and delivers actionable intelligence into LLM issues

LLM monitoring

Evaluate prompt & response integrity

With LLM monitoring your team can easily uncover data and integrity issues and actionable insights on your prompts and responses. Get granular visibility into readability, sentiment, and language mismatches, investigate responce quality and session feedback data, and evaluate distribution shifts in your LLM’s over time.

LLM monitoring of prompt and responce integrity

Detect data, concept & retrieval drift

Meet operational drift metrics for LMM monitoring — a production-first approach to identifying and debugging behavior changes in your LLM.

  • Data drift for LLMs: Break down prompts and responses into language components and track over time.
  • Concept drift for LLMs: Identify changes in usage such as, task drift and topic drift.
  • Retrieval drift for LLMs: Leveraging a vector database or fine-tuning with an internal corpus? See over time if responses are drifting from your benchmarks.
LLM monitoring for task type drift

Check out Elemeta!
Our open-source package for unstructured data

Pinpoint & analyze hallucinations

Is your LLM responding with the relevant context? Or answering questions outside of its train date? Superwise pinpoints potential hallucination indicators so you can push them to a reviewer or even block the response altogether.

LLM monitoring for potential hallucinations

Identify AI governance & privacy violations

Stay on top of AI governance and privacy violations with a suite of metrics built to identify bias, profanity, forbidden patterns such as PII and PHI data, and much more, and alert the relevant risk and compliance teams in real-time of violations so they can take action.

LLM monitoring of PII violation

Uncover malicious use & adversarial attacks

Are you worried about bad actors accessing proprietary information or influencing your LLM’s outcomes? Superwise zeros in on data poisoning, jailbreaking, and prompt injection and leaking attacks. Providing you with insight into the potential root cause and their impact on your LLM, so you can re-engineer your prompts and learning processes to block future attacks.

LLM monitoring for adversarial attacks

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