Dynamic AI Agents with LangGraph, Prompt Engineering, and RAG

Offered By: Data Centric via YouTube

Course Description

Overview

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Explore the development of dynamic AI agents using LangGraph, advanced prompt engineering techniques, and Retrieval-Augmented Generation (RAG) in this comprehensive video presentation. Learn how to create powerful agents for long-running, research-intensive tasks by combining chain-of-reasoning and meta-prompting with RAG. Discover the capabilities of Jar3d, an AI agent with internet access that enhances tasks such as newsletter creation, literature reviews, and holiday planning. Gain insights into Jar3d's architecture, code structure, and orchestration using LangGraph. Delve into prompt engineering strategies and evaluate the strengths and weaknesses of this approach. Follow along with demonstrations, code overviews, and discussions on practical applications in AI engineering and research-intensive activities.

Syllabus

Introduction:
Jr3d Demo
Jar3d Architecture:
Overview of Jar3d code:
Prompt Engineering:
Reviewing Jar3d Newsletter:
Strengths & Weaknesses:

Taught by

Data Centric

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