Continuously Validate Your
LLM-Based Applications

Deepchecks LLM evaluation platform designed to validate LLMs before they are deployed as well as in production. Easily evaluate the correctness, robustness & bias characteristics of the LLM-based applications, and monitor their performance over time.

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Integrations

Why LLM Validation?

LLMs can hallucinate, generate inaccurate or misleading responses, and can introduce unpredicted anomalies that may cause disruptions in production.

LLM Evaluation

The Deepchecks LLM Evaluation module focuses on the pre-deployment phase, from the first viable version of your LLM-base application all the way through version comparison and internal experiments. It provides a thorough evaluation of LLM model characteristics, performance metrics, and potential pitfalls - based both on manual annotations and on properties calculated by Deepchecks’ open-source engine.

LLM Monitoring

Monitoring Large Language Models (LLMs) is critical for ensuring optimal performance, identifying drift, and ensuring compliance with regulation, internal policies and soft laws. These rigorous checks ensure your LLMs consistently deliver optimal performance and giving visibility to everything in your LLM pipeline.

LLM Gateway Coming Soon

Safeguard your models with our LLM Gateway, a real-time barrier against harmful outputs. The LLM Gateway scans the inputs and outputs in real-time, enabling blocking of harmful content as well as re-routing specific inputs to another model or script when certain conditions are met.

Why Continuous LLM Validation?

Comprehensive Evaluation

Deepchecks provides a holistic solution
towards evaluating your LLMs, based
on a combination of manual annotations
and “AI for AI” models.This includes
analysis of the content, the style,
and potential red flags.

Real-Time Monitoring

Deepchecks enables real-time tracking
of your LLM’s performance in
production, notifying you of deviations,
drifts, or anomalies on data that would
otherwise be 100% unstructured.

LLM Gateway

Deepchecks safeguards your LLM
model from generating toxic and
harmful responses in real time. This
enables blocking of undesired outputs
as well as re-routing specific inputs in
pre-defined cases.

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