LLM monitoring

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

LLM monitoring for potential hallucinations

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.

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

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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.

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.

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.

Try the community edition

No credit card required. FREE forever.

1

Sign up for Superwise

Easily get started with a free community edition account.

!pip install superwise

2

Build your project

import superwise as sw
project = sw.project(“Fraud detection”)

model = sw.model(project,“Customer a”)

policy = sw.policy(model,drift_template)

3

Start monitoring

Fraud detection

Entire population drift – high probability of concept drift. Open incident investigation →

Fraud detection

Segment “tablet shoppers” drifting.
Split model and retrain.

FREE community edition

Featured resources

Blog

Monitoring NLP with Superwise & Elemeta

In this post, we’re going to show you an example of how to use Elemeta together with Superwise’s model observability community edition to supply visibility and monitoring of your NLP model’s input text.

Elemeta: Extract metafeatures from unstructured data

We’re excited to release into beta v1.0 of Elemeta, our open-source library for exploring, monitoring, and extracting features from unstructured data.

Challenges of NLP monitoring

Monitoring ML, in general, is not trivial – NLP monitoring, in particular, produces a few unique challenges that we’ll examine in this post.