How to Build an Agentic Decision-Tree RAG System with Intelligent Query Routing, Self-Checking, and Iterative Refinement In this tutorial, we develop an advanced Agentic Retrieval-Augmented Generation (RAG) system designed to intelligently route queries to the appropriate knowledge sources, perform self-checks to evaluate answer quality, and iteratively refine responses for improved accuracy. We will implement this… →

Meet ‘kvcached’: A Machine Learning Library to Enable Virtualized, Elastic KV Cache for LLM Serving on Shared GPUs Understanding the Target Audience The primary audience for kvcached includes machine learning engineers, data scientists, and IT decision-makers in enterprises focused on deploying large language models (LLMs) at scale. These professionals are often dealing with the challenges… →

«`html 5 Common LLM Parameters Explained with Examples Large language models (LLMs) offer several parameters that let you fine-tune their behavior and control how they generate responses. If a model isn’t producing the desired output, the issue often lies in how these parameters are configured. In this tutorial, we’ll explore some of the most commonly… →

«`html How to Build, Train, and Compare Multiple Reinforcement Learning Agents in a Custom Trading Environment Using Stable-Baselines3 In this tutorial, we explore advanced applications of Stable-Baselines3 in reinforcement learning. We design a fully functional, custom trading environment, integrate multiple algorithms such as PPO and A2C, and develop our own training callbacks for performance tracking.… →

A New AI Research from Anthropic and Thinking Machines Lab Stress Tests Model Specs and Reveal Character Differences among Language Models Understanding the Target Audience The target audience for this research includes AI researchers, developers, business managers, and decision-makers in organizations that utilize language models (LLMs). Their pain points revolve around ensuring the reliability and… →

Understanding the Target Audience The target audience for the tutorial on building a computer-use agent encompasses developers, data scientists, AI enthusiasts, and business professionals interested in integrating AI with productivity. These individuals often have technical backgrounds and are inclined toward project-based learning. Their primary goals include improving automation capabilities, enhancing workflow efficiency, and leveraging local… →