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Safeguard AI Built on MosaicML Seamlessly in Databricks

Embeddings aren't enough. Take a data centric approach to LLMOps!

LangKit uses patent pending natural language techniques to extract actionable insights about prompts and responses. Using these insights, you can identify and mitigate malicious prompts, sensitive data, toxic responses, problematic topics, hallucinations, as well as jailbreak attempts in any LLM model.

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Control which prompts and responses are appropriate for your LLM application in real time. Define a set of boundaries that you expect your LLM to stay within, detect problematic prompts and responses based on a range of metrics and take appropriate action in the case of a failure.

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Validate how your LLM responds to known prompts both continually as well as ad-hoc, to ensure consistency when modifying prompts or changing models. Evaluate and compare the behavior of multiple models on the golden set of prompts over a range of metrics. 

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Observe your prompts and responses at scale by extracting key telemetry data and compare against smart baselines over time. Observability helps ensure you spend your time on quality signals when debugging or fine-tuning the LLM application experience.

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Large Language Models are just that, large. LangKit is built for massive scale.

Monitor and safeguard MosaicML and other LLMs hosted on Databricks.

WhyLabs makes it easy to monitor and safeguard MosaicML and other LLMs hosted on Databricks utilizing our industry standard for LLM monitoring, LangKit. LangKit detects and prevents malicious prompts, toxicity, hallucinations, and jailbreak attempts. 

LangKit Tutorial
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Understand and track the behavior of any LLM by extracting 50+ out-of-the-box telemetry signals

QUALITY: Are your prompts and responses high quality (readable, understandable, well written)? Are you seeing a drift in the types of prompts you expect or a concept drift in how your model is responding?

RELEVANCE: Is your LLM responding in with relevant content? Are the responses adhering to the topics expected by this application? 

SENTIMENT: Is your LLM responding in the right tone? Are your upstream prompts changing their sentiment suddenly or over time? Are you seeing a divergence from your anticipated topics?

SECURITY: Is your LLM receiving adversarial attempts or malicious prompt injections? Are you experiencing prompt leakage?

LangKit and Databricks LLM Diagram

Equip your team with the necessary tools for responsible LLM development

Large Language Models have the potential to transform every business, and new use cases are emerging every day. At WhyLabs, we are partnering with organizations across Healthcare, Logistics, Banking, and E-commerce to help ensure that LLM applications are implemented in a safe and responsible manner.

Whether you're an LLM researcher, thought leader, or practitioner pushing the boundaries of what's possible today, let's connect. We'd love to partner and drive the standardization of LLMOps!

Help us drive standardization of LLMOPS
LangKit quote from Alan Descoinss the Tryolabs CTO
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