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Hands-on LangGraph course repo for building production-grade LLM agents with Agentic RAG, ReAct, and reflection workflows.

This repository contains all the code, notebooks, and resources from my free YouTube course on Generative AI using OpenAI. The course covers everything from API fundamentals and retrieval-augmented generation (RAG) to building intelligent agents, evaluating RAG systems with RAGAS, fine-tuning OpenAI models, and preparing Gen AI apps for production.

Built an agentic RAG-based course planning assistant grounded in academic catalogs. Supports prerequisite reasoning, multi-hop retrieval, and term planning with verifiable citations. Includes evaluation suite, safe abstention, and structured outputs for production-grade ML system design.

🤖 Mastering Generative AI & LLMs - 8-Week Course Projects Hands-on implementations: RAG systems, fine-tuned models, autonomous agents & real-world AI apps. Features web scraping, multi-modal chatbots, code optimization (60,000x boost) & production deployment. Tech: HuggingFace • LangChain • QLoRA • Vector DBs

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This repository contains Lab & notes for my course "Agentic RAG for Production"

Repo for Udemy Course: Build AI Agents with LangChain and LangGraph RAG, Tools, MCP and Production-Ready Agentic AI Systems (Python)

Generative AI Crash Course - A 1-week intensive course for PhD statisticians covering foundation models, transformers, RAG, agents, and production deployment

A structured 8-part course on building multi-agent AI systems using local LLMs, RAG, orchestration patterns, and production-grade architecture.

Production AI agent tutorial project - OpenAI, RAG, vector DB, tool-calling, memory, and evaluation framework. Course by Scott Moss for Frontend Masters

Agent Engineering Foundations, an open-source course teaching you how to design, orchestrate, and deploy AI agents. Covers automation agents, digital co-workers, tools, MCP, reasoning, RAG, observability, and production best practices.

AI-Native Development Course — Zero to production AI engineer in 50 chapters. Covers LLMs, RAG, agents, fine-tuning, evals, and system design with runnable Python labs and interactive diagrams.

A production-grade event-driven, multi-agent AI orchestration microservices platform that autonomously generates grounded, structured educational courses from raw unstructured documents using RAG and LLM-as-a-Judge evaluation.

Personal notes, code examples, and projects from the 8-week LLM Engineering course by Ed Donner. Covers prompts, fine-tuning, vector databases, RAG (Retrieval-Augmented Generation), agents, and production deployment strategies.

From Full Stack to AI Engineer — a self-paced, milestone-based course built for backend JavaScript developers. Learn LangChain, RAG pipelines, LangGraph agents, MCP, and production AI systems — all in TypeScript.

A production-grade, agentic, course-aware AI academic platform for university-level sciences — combining Retrieval-Augmented Generation (RAG), LangGraph-based decision logic, structured academic data models, and local LLM inference. Now fully deployed on AWS infrastructure for scalable, secure, and reliable academic assistance.

his project is a sophisticated, production-ready implementation of an **Agentic Retrieval-Augmented Generation (RAG)** system. Built for the UT Dallas 2025 course catalog ecosystem, the assistant functions as a fully autonomous academic advisor that grounds 100% of its reasoning in official, scraped catalog facts.

EduMind is a production‑ready, AI‑powered personalized learning assistant built on the Endee open‑source vector database and Groq’s LLaMA 3.3, designed to turn course materials into a semantic, searchable knowledge base with RAG Q&A, recommendations, and an agentic study planner.