Dear Reader,

As enterprise adoption of large language models accelerates, one key challenge is emerging - how can developers trust that their LLM applications will perform reliably in production? Meet Confident AI, an SF-based startup aiming to instil confidence in AI through intelligent testing and evaluation.

Confident AI offers an open-source Python package that allows developers to evaluate their LLM apps against real-world sample data. The package is one of the largest in the evaluation space and offers a range of statistical-based, model-based, and even LLM-based metrics to enable unit and regression testing by comparing outputs against expected results. This prevents breaking changes and enables developers to "fine-tune" hyperparameters, such as prompt templates, more easily. Metrics assess factual consistency, answer relevancy, etc., and come with an option to create custom metrics that are automatically integrated with Confident AI's ecosystem.

While the open-source library leverages the latest research in ML to provide offline testing in development, Confident AI's hosted platform brings additional benefits like centralized logging, debugging aids, and tracking of top prompts and contexts. Later this month they'll be launching production-level monitoring as well.

Confident AI wants to be the standard for reliability and trust in AI-powered applications. They already attracted paying enterprise clients across industries like cloud infrastructure, sales enablement and customer support.

By taking the fear out of deploying conversational AI, Confident AI empowers developers to build the next generation of intelligent applications. With rigorous testing and evaluation, enterprises can finally have confidence in rolling out ambitious AI projects. The future is bright, with startups like Confident AI paving the way.

You can try it for free.

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