Chain-of-thought (CoT) prompting has become a popular method for improving and interpreting the reasoning processes of large language models (LLMs). The idea is simple: if a model explains its answer step-by-step, then those steps should give us some insight into…
As autonomous AI agents move from theory into implementation, their impact on the financial services sector is becoming tangible. A recent whitepaper from IBM Consulting, titled “Agentic AI in Financial Services: Opportunities, Risks, and Responsible Implementation”, outlines how these AI…
Recent developments have shown that RL can significantly enhance the reasoning abilities of LLMs. Building on this progress, the study aims to improve Audio LLMs—models that process audio and text to perform tasks like question answering. The MMAU benchmark is…
Ever wondered why your neural network seems to get stuck during training, or why it starts strong but fails to reach its full potential? The culprit might be your learning rate – arguably one of the most important hyperparameters in…
Behavioral economist Sendhil Mullainathan has never forgotten the pleasure he felt the first time he tasted a delicious crisp, yet gooey Levain cookie. He compares the experience to when he encounters new ideas. “That hedonic pleasure is pretty much the…
The Model Context Protocol (MCP) represents a powerful paradigm shift in how large language models interact with tools, services, and external data sources. Designed to enable dynamic tool invocation, the MCP facilitates a standardized method for describing tool metadata, allowing…
Recent progress in LLMs has shown their potential in performing complex reasoning tasks and effectively using external tools like search engines. Despite this, teaching models to make smart decisions about when to rely on internal knowledge versus search remains a…
Language models trained on vast internet-scale datasets have become prominent language understanding and generation tools. Their potential extends beyond language tasks to functioning as decision-making agents in interactive environments. When applied to environments requiring action choices, these models are expected…
Recent advancements in LM agents have shown promising potential for automating intricate real-world tasks. These agents typically operate by proposing and executing actions through APIs, supporting applications such as software engineering, robotics, and scientific experimentation. As these tasks become more…
In this tutorial, we demonstrate how to build a powerful and intelligent question-answering system by combining the strengths of Tavily Search API, Chroma, Google Gemini LLMs, and the LangChain framework. The pipeline leverages real-time web search using Tavily, semantic document…
