Today, we’re excited to announce the general availability of the Contextual AI Platform. This is the first enterprise platform designed for building specialized RAG agents to support expert knowledge work. What is a specialized RAG agent? First, a general-purpose AI agent is one designed to automate simple daily tasks like scheduling a meeting or responding to an email. On the other hand, a specialized RAG agent is one designed to augment subject-matter experts performing complex domain-specific work. The Contextual AI Platform allows you to create these agents easily and achieve SOTA accuracy right out of the box. Check out what the Contextual AI Platform can do.

Introducing LMUnit: Natural language unit testing for LLM evaluation How do you really know if your language model is behaving the way you expect? When evaluation is this critical, your best methodology shouldn't just be vibes. With SOTA results on FLASK & BigGenBench and top-10 on RewardBench, LMUnit brings the rigor and familiarity of traditional software engineering unit testing to LLM evaluation. Read on to learn how we built it and try it for free using our API 👇 🔗 🧵 (1/5)

Aligning Language Models with preferences leads to stronger and safer models (GPT3 → ChatGPT). However, preferences (RLHF) contain irrelevant signals, and alignment objectives (e.g. DPO) can actually hurt model performance. We tackle both, leading to a ~2x performance boost.

Today, we’re excited to announce RAG 2.0, our end-to-end system for developing production-grade AI. Using RAG 2.0, we’ve created Contextual Language Models (CLMs), which achieve state-of-the-art performance on a variety of industry benchmarks. CLMs outperform strong RAG baselines built using GPT-4 and top open-source models like Mixtral, according to our research and customers. Read more in our blog post: