RAG-powered conversational research assistants address the limitations of traditional language models by combining them with information retrieval systems. The system searches through specific knowledge bases, retrieves relevant information, and presents it conversationally with proper citations. This approach reduces hallucinations, handles domain-specific knowledge, and grounds responses in retrieved text. In this tutorial, we will demonstrate building… →

A critical advancement in recent times has been exploring reinforcement learning (RL) techniques to improve LLMs beyond traditional supervised fine-tuning methods. RL allows models to learn optimal responses through reward signals, enhancing their reasoning and decision-making capabilities. RL introduces a feedback-driven training loop that better aligns with human-like learning processes, particularly in tasks involving step-by-step… →

LLMs are advancing rapidly across multiple domains, yet their effectiveness in tackling complex financial problems remains an area of active investigation. The iterative development of LLMs has significantly driven the evolution of artificial intelligence toward artificial general intelligence (AGI). OpenAI’s o1 series and similar models like QwQ and Marco-o1 have improved complex reasoning capabilities by… →

Large language models (LLMs) are rapidly transforming into autonomous agents capable of performing complex tasks that require reasoning, decision-making, and adaptability. These agents are deployed in web navigation, personal assistance, and software development. To act effectively in real-world settings, these agents must handle multi-turn interactions that span several steps or decision points. This introduces the… →