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Elite ML Engineer from Day 1
·
Module 11
Agentic AI Systems and Orchestration
1
Introduction to ReAct: LLM Reasoning and Tool Interaction
Explain the ReAct (Reason, Act) framework for enabling LLMs to reason and interact with external tools.
2
Function Calling for LLMs
Implement tool use (function calling) for an LLM using a standardized API schema.
3
Crafting Custom Tool APIs for LLMs
Design a custom tool API, including a clear description, function signature, and input/output schemas for the LLM to use.
4
Developing Custom Tool Backends
Implement the backend logic for a custom tool as a callable function.
5
Building Multi-Step Agents with Orchestration Frameworks
Build a multi-step agent that uses multiple tools to accomplish a complex goal using an orchestration framework (e.g., LangGraph, AutoGen).
6
Building Short-Term Conversational Memory
Implement short-term conversational memory to maintain context within an agentic session.
7
Agentic Planning for Complex Requests
Design an agentic workflow that includes a planning step to decompose a complex user request into a sequence of sub-goals.
8
Understanding the Model Context Protocol (MCP)
Describe the purpose of the Model Context Protocol (MCP) for standardizing agent-tool communication.
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Retrieval-Augmented Generation Systems
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LLM Operations: Deployment, Inference, and Monitoring