WhyLabs, the leading observability platform trusted by high-performing teams to control the behavior of AI & data applications, announced LangKit – the observability and safety standard for Large Language Models (LLMs). LangKit enables detection of risks and safety issues in open-source and proprietary LLMs, including toxic language, jailbreaks, sensitive data leakage, and hallucinations.
“As more organizations incorporate LLMs into customer-facing applications, reliability, and transparency will be key for successful deployments,” said Andrew Ng, Managing General Partner of AI Fund. “With LangKit, WhyLabs provides an extensible and scalable approach for solving challenges that many AI practitioners will face when deploying LLMs in production.”
“With the emergence of LLMs, the AI community faced a unique phenomenon: our ability to evaluate the performance of this new wave of AI technologies is increasingly challenged. At WhyLabs, we have been working with the industry’s most advanced AI/ML teams for the past year to build an approach for evaluating and monitoring generative models; these efforts culminated in the creation of LangKit,” said Alessya Visnjic, co-founder and CEO at WhyLabs.
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With LangKit, AI practitioners extract a critical set of telemetry data from prompts and responses to describe the behavior of an LLM. The WhyLabs Platform enables users to set alert parameters for activity, including malicious prompts, sensitive data, toxic responses, problematic topics, hallucinations, as well as jailbreak attempts. With these alerts and guardrails, application developers can prevent inappropriate prompts, undesirable LLM responses, and violations of LLM usage policies.
“In an era in which AI transitioned from buzzword to vital business necessity, effective use of LLMs is a must. As our team at Tryolabs helps enterprises put this powerful technology into practice, safety remains one of the main blocks for widespread adoption,” said Alan Descoins, CTO at Tryolabs, who specializes in helping enterprises accelerate their adoption of AI. “WhyLabs’ LangKit is a leap forward for LLMOps, providing out-of-the-box tools for measuring the quality of LLM outputs and catching issues before they affect tasks downstream — whether end users, other applications, or even other LLMs. The fact that it’s easily extensible and lets you add your own checks is also a big plus!”
“At Symbl.ai we deliver conversation intelligence as a service to builders, so observability is critical for smooth operations and excellent customer experience. Our platform enables experiences powered by both Understanding and Generative AI for which LangKit is critical to enable transparency and governance required across the end-to-end AI stack.,” said Surbhi Rathore, CEO of Symbl.ai.
SOURCE: Businesswire