Whether you’re testing different models, iterating on prompts, or validating outputs against ground truth data, our platform makes evaluating AI applications so easy it’s a no-brainer to integrate it in your routine developer workflows. ...
AI evaluation is a critical component in building reliable, impactful models. We’re excited to introduce judges, an open-source library of LLM-as-a-judge evaluators designed to help bootstrap your evaluation process. Complementing judges is autojudge, an extension that automatically creates evaluators aligned with human feedback. ...
Quotient and MongoDB Atlas integrate seamlessly to provide an end-to-end solution for implementing, evaluating and shipping RAG-powered applications. Created by members of the team that built the evaluation infrastructure of GitHub Copilot, Quotient is grounded in first-hand knowledge of what is needed to create and ship powerful generative AI products. Quotient connects the dots between intention and outcome in LLM product development. And through rapid, data-driven experimentation grounded in real-world scenarios, Quotient enables developers to conceptualize, assess, and enhance products tailored to the specific needs of their users. ...
This collaboration marks a significant step forward in AI development by offering developers a comprehensive, end-to-end solution for building, evaluating, and shipping Large Language Model (LLM) products using Retrieval-Augmented Generation (RAG). By combining Quotient’s state-of-the-art generative AI evaluation technology with Qdrant’s cutting-edge vector search, AI developers can gain the tools they need to bring best-in-class AI products to market. ...
Generative AI has been hailed as a disruptive force, but it has fallen short of expectations. Organizations have invested heavily in hopes of reaping transformative benefits, yet widespread adoption and real impact have failed to materialize and match the hype. ...
