Dynamic AI Agents with LangGraph, Prompt Engineering Enhancements + RAG

Combining prompt-engineering techniques such as chain-of-reasoning and meta-prompting with Retrieval-Augmented Generation (RAG) on the fly has enabled me to develop a powerful agent for long-running, research-intensive tasks. Jar3d has internet access and significantly enhances tasks like creating newsletters, writing literature reviews, planning holidays, and other research-intensive activities. I will demonstrate Jar3d and explain how it operates at a high level. Jar3d is orchestrated with LangGraph. Need to develop some AI? Let's chat: https://calendly.com/john-brainqub3/30min Register your interest in the AI Engineering Take-off course: https://www.data-centric-solutions.com/course Hands-on project (build a basic RAG app): https://www.educative.io/projects/build-an-llm-powered-wikipedia-chat-assistant-with-rag Stay updated on AI, Data Science, and Large Language Models by following me on Medium: https://medium.com/@johnadeojo Jar3d GitHub repo: https://github.com/brainqub3/meta_expert Meta Prompting Research Paper: https://arxiv.black/pdf/2401.12954 Professor Synapse: https://github.com/ProfSynapse/Synapse_CoR Chapters Introduction: 00:00 Jr3d Demo 02:49 Jar3d Architecture: 18:27 Overview of Jar3d code: 23:39 Prompt Engineering: 31:45 Reviewing Jar3d Newsletter: 44:20 Strengths & Weaknesses: 58:43