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

Tutorials and exercises are done in the course 'Building RAG Agents with LLM'

A curated list of the best AI resources, courses, tools, and repositories. Covering Generative AI (GenAI), LLMs, RAG, AI Agents, Prompt Engineering, and more. Your go-to cheat sheet for all things AI.

Hands-on course for building autonomous AI agents in Go: LLM fundamentals, tool/function calling, RAG, evals, multi-agent, safety, and observability

Code and resources for the course named Building RAG Agents with LLMs by NVIDIA

This repository contains my hands-on solutions and code implementations for the IBM RAG and Agentic AI Professional Certificate course. All official labs and exercises are reproduced and adapted here, with a focus on running them using large language models (LLMs) available in mainland China, such as ZhipuAI's ChatGLM series.

Capstone Project for a Google-Kaggle Gen AI Intensive Course - Youtube Comment Analyzer using Agentic LLMs, Embedding and Vectorization, RAG

A complete roadmap to become an AI/LLM Engineer in 2026. Free & paid courses, 5 portfolio projects, interview prep, and resources. For software engineers with Python & ML basics who want to break into AI. Covers Transformers, RAG, fine-tuning, agents, LangChain, MLOps, and deployment.

Capstone project for NVIDIA building RAG agents using LLM course. (certification attained)

Learning materials for the course Building RAG Agents with LLMs organized by NVIDIA.

Hands-on 2-day course for building LLM agents with RAG, memory, tools, and multi-agent architectures.

second pass of Food Finder Rag app for UCB LLM Agent Course without key

AI Developers Course (March 2026) — Python environment scaffold with Devbox + uv + direnv for ML, NLP, LLMs, RAG, and agents

this is repo will teach you about langchain , LLM , rag , agents ,access memory and a real time project for this course

This course teaches practical deployment of retrieval-based LLMs. Learn to compose predictable LLM systems, design dialog and document reasoning, leverage embeddings for retrieval and guardrailing, and implement RAG agents for question answering. Topics include LLM interfaces, LangChain, embeddings, and vector stores.

Repository for the 2025 AI Project I2A2 Course. Includes materials, challenges, and group projects on LLMs, Python, RAG, and multi-agent systems. Follows course calendar from April to November 2025.

AI-powered curation agent for dynamic university courses or subjects. Uses LLMs and RAG to monitor course documentation, detect inconsistencies, reduce redundancy, and suggest improvements across cohorts, keeping instructors in control with human-in-the-loop approval.

n agentic RAG chatbot powered by a Gemini LLM. It intelligently routes user queries to a Neo4j knowledge graph for detailed skill paths with course links, or to a FAISS vector database for general FAQs, ensuring precise, context-aware answers.

An agentic RAG LLM system for retrieving knowledge from course materials

Source code for "Complete GenAI with Java & Spring AI: LLMs, RAG, AI Agents" Udemy course

small study project on course "LLMs and RAG for developers". Use folowwing themes: rag (by langchain), stateful agents (by langgraph)

12-week LLM learning course for CS graduates - 60 articles covering Transformer, RLHF, Agents, RAG, and more

This repo is for my learning outcomes from the Udemy course AI Engineer Core Track: LLM Engineering, RAG, QLoRA, Agents by Ed Donner

Project for scalable ML course at KTH which embeds daily preprints and uses them to power a RAG-LLM Agent

Study repository for the TIC em Trilhas course (PUC-Rio), focusing on RAG architectures, LangGraph, and LangChain to develop advanced AI agents and local LLM workflows.

This is a hands-on training course designed for developers. In this course we cover how to work with LLM, implement RAG pipelines, integrate LangChain for orchestration, and design AI agents capable of handling real tasks.

This repository contains materials for The AI Engineering Bootcamp, a 13-week cohort-based course focused on building and deploying LLM applications. Key topics include: Prompt Engineering, Retrieval-Augmented Generation (RAG), Fine-Tuning, Agents, LLM Ops

Final Project for the GenAI & LLM Course at the Technion. A multi-agent recruitment assistant that screens candidates, answers FAQs using RAG, and schedules interviews.

This repository contains a crash course on Langchain, a framework for developing applications using large language models (LLMs). The course covers various aspects of working with LLMs, including chat models, prompt templates, chains, retrieval augmented generation (RAG), agents, and tools. It also includes examples of using different LLM providers

Multimodal RAG-Based Multi-Agent Framework for Legal Document Question Answering with LLMs and OCR Integration built as part of the Generative AI and its Applications course in the 6th semester.