Agentic LLM Systems and Memory-Augmented Architectures
Construct sophisticated Large Language Model (LLM) applications capable of autonomous reasoning, planning, and interaction. This course details the design and implementation of agentic systems, focusing on advanced architectures like ReAct and Tree of Thoughts, and integrating diverse memory mechanisms for stateful, long-context operations. Gain practical experience in building agents that can utilize tools, collaborate, and solve complex problems through structured reasoning processes. Suitable for engineers and researchers aiming to push the boundaries of LLM capabilities.
Prerequisites: Extensive experience with LLM fundamentals (transformer architecture, fine-tuning), deep learning frameworks (PyTorch or TensorFlow), and advanced Python programming.
Level: Expert
What You'll Learn
Agent Architectures
Analyze and implement advanced agent architectures including ReAct, Self-Ask, and Tree of Thoughts.
Memory Augmentation
Design and integrate various memory systems (short-term, long-term, vector databases) into LLM applications.
Reasoning and Planning
Implement complex reasoning and planning algorithms for autonomous task decomposition and execution.
Tool Integration
Develop mechanisms for LLM agents to effectively select and utilize external tools and APIs.
Multi-Agent Systems
Construct and manage systems involving multiple interacting LLM agents for collaborative or competitive tasks.
System Evaluation
Apply rigorous methods for evaluating the performance, reliability, and robustness of agentic systems.
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