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If you’ve ever tried developing–or harder yet, productionizing–an LLM application, you know that getting things to work as intended is not as easy as you think. Excluding demos on X,…
October 17th, 2024 10:00am PST – 11:00am PST Virtual Join us as we walk through how to build AI assistants using Agentic Retrieval-Augmented Generation (RAG). You’ll learn how…
Welcome to our regular update on new releases, enhancements, and changes. What’s New Embeddings Tracing With Embeddings Tracing, you can effortlessly select embedding spans and dive straight into the UMAP…
Thanks to the Vectara team for contributing this post! Introduction Retrieval-Augmented Generation (RAG) is a framework that enhances the capabilities of large language models (LLMs) by integrating external information retrieval…
Building a good agent is hard. Setting up a basic agent is straightforward, especially if you use a framework and a common architecture. However, the difficulty lies in taking that…
When building and scaling LLM-based applications, ensuring model performance is critical. One powerful method for evaluating that performance is using an LLM as a judge. This allows you to systematically…
In the evolving landscape of artificial intelligence, agentic systems—autonomous agents capable of making decisions and learning from feedback loops in their environment—are becoming increasingly sophisticated. At the same time, as…
Co-authored by Aparna Dhinakaran 2024 has been dubbed as the year of Agents. The year where we move beyond simple RAG flows and enter into the promised land of autonomous…
OpenAI recently released its o1-preview, which they claim outperforms GPT-4o on a number of benchmarks. These models are designed to think more before answering and handle complex tasks better than…
Discover the Ultimate Guide to Safeguarding Your AI Applications As large language model (LLM) applications become widespread, so do the risks of harmful outputs, jailbreak attempts, and unexpected failures. “LLM…
