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Introduction to LLM Agents | NVIDIA Technical Blog

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Consider a large language model (LLM) application that is designed to help financial analysts answer questions about the performance of a company. With a well-designed retrieval augmented generation (RAG) pipeline, analysts can answer questions like, “What was X corporation’s total revenue for FY 2022?” This information can be easily extracted from financial statements by a seasoned analyst. Now consider a question like, “What were the three takeaways from the Q2 earnings call from FY 23? Focus on the technological moats that the company is building”. This is the type of question a financial analyst would want answered to include in their reports but would need to invest time to answer. How do we develop a solution to answer a question like above? It is immediately apparent that this information requires more than a simple lookup from an earnings call. This inquiry requires planning, tailored focus, memory, using different tools, and breaking down a complex question into simpler sub-pa

Introduction to LLM Agents | NVIDIA Technical Blog Technical Blog Subscribe Related Resources Agentic AI / Generative AI English 한국어 Introduction to LLM Agents Nov 30, 2023 By Tanay Varshney Like Discuss (0) L T F R E AI-Generated Summary Like Dislike LLM-powered agents are systems that use large language models to reason through problems, create plans, and execute tasks with the help of various tools. These agents consist of key components, including an agent core, memory module, tools, and planning module, which work together to enable complex reasoning and task execution. Examples of enterp

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