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Generative AI systems are powerful tools that can create new content based on data and rules. However, they are not flawless and can sometimes produce inaccurate or misleading results. This can pose serious risks for enterprises that rely on generative AI for their internal operations, especially in domains like financial services.
To ensure the reliability and security of generative AI systems, enterprises need to evaluate and test them regularly. This can be a challenging and time-consuming task, as generative AI systems can have complex and unpredictable behavior.
Fortunately, there is a solution that can simplify and automate the process of generative AI testing. Patronus AI, an automated evaluation and security platform, has announced a partnership with MongoDB, a database platform, to bring automated large language models (LLM) evaluation and testing capabilities to enterprise customers.
What are LLMs and why do they need testing?
Large language models (LLMs) are a type of generative AI system that can produce natural language content based on data and rules. LLMs can be used for various applications, such as text summarization, chatbots, content generation, and more.
However, LLMs are not perfect and can sometimes generate content that is incorrect, irrelevant, or harmful. For example, LLMs can hallucinate, which means they can invent facts or details that are not supported by the data. LLMs can also struggle with reasoning and numerical calculations, which can lead to errors or inconsistencies.
These issues can have serious consequences for enterprises that use LLMs for their internal operations. For instance, if an LLM generates a false or misleading report, it can affect the decision-making process and the reputation of the enterprise. Therefore, enterprises need to test and monitor their LLMs regularly to ensure their quality and security.
How can Patronus AI and MongoDB help with generative AI testing?
Patronus AI and MongoDB have partnered to offer a solution that can help enterprises with generative AI testing. By combining the strengths of Patronus AI and MongoDB’s Atlas Vector Search product, the partnership seeks to address the challenges faced by enterprises in the realm of generative AI testing.
Patronus AI is an automated evaluation and security platform that can test and monitor LLMs for various metrics, such as accuracy, relevance, coherence, consistency, and safety. Patronus AI can also detect and prevent hallucinations and other unexpected LLM behavior.
MongoDB is a database platform that can store and manage large amounts of data. MongoDB’s Atlas Vector Search product is a feature that can enable fast and flexible search capabilities for document-based data. Atlas Vector Search can help enterprises build and deploy LLM workflows that can generate and retrieve natural language content.
With the support of MongoDB Atlas, customers can use Patronus AI to evaluate, test, and monitor their LLM workflows. This can help enterprises ensure the reliability and security of their generative AI systems, as well as improve their performance and efficiency.
“Enterprises are excited about the potential of generative AI testing, but they are concerned about hallucinations and other unexpected LLM behavior,” said Anand Kannappan, CEO and co-founder of Patronus AI. “We are confident our partnership with MongoDB will accelerate enterprise AI adoption.”
AI is becoming an increasingly popular tool, but still needs a human touch to operate properly. As reported, in the middle of many enterprise concerns around the use of innovative AI solutions are questions around the data used to train the AI models, as well as protections around that data’s provenance and security.
Generative AI testing is a crucial step to ensure the quality and safety of AI solutions. With Patronus AI and MongoDB, enterprises can have a simple and effective way to test and monitor their generative AI systems.
