Generative AI Engineering: LLMs, RAG, and Agentic Systems
Category: Tutorials
- views: 131
- date: 5 October 2026
- posted by: 0nelove
Generative AI Engineering: LLMs, RAG, and Agentic Systems
Last updated 1/2026
Created by Rajeev Sakhuja
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English + subtitle | Duration: 269 Lectures ( 28h 15m ) | Size: 9.2 GB
Learn to design and implement GenAI workflows, multi-agent systems, LangChain, LangGraph, MCP, and model fine-tuning
What you'll learn
⚡ Master Generative AI foundations, how LLMs work, and how modern AI systems are designed and applied in real-world products.
⚡ Design and build end-to-end Generative AI systems using LLMs, retrieval pipelines, tools, and agentic workflows.
⚡ Implement Retrieval-Augmented Generation (RAG), embeddings, vector search, reranking, and advanced retrieval patterns.
⚡ Build AI agents, multi-step reasoning systems, and multi-agent workflows using LangChain and LangGraph.
⚡ Develop production-style applications with structured outputs, validation, memory, and human-in-the-loop workflows.
⚡ Create MCP servers and clients to connect LLMs to real tools, services, and enterprise systems.
⚡ Fine-tune and optimize models using Hugging Face workflows, dataset preparation, and quantization techniques.
⚡ Apply system-level best practices for cost, reliability, scalability, and responsible deployment of GenAI applications.
Requirements
❗ Comfortable with basic Python programming
❗ Familiarity with APIs, JSON, and software development fundamentals
❗ Ability to install tools and work in a local development environment (Windows, Mac, or Linux)
❗ No prior machine learning or data science background required
Description
Build Real-World Generative AI Systems with LLMs, RAG, and AI Agents
Go beyond prompts and chatbots. This course takes you on acomplete, progressive journey fromGenerative AI fundamentals to advanced system-level techniques.
You'll start by mastering thecore concepts of LLMs, NLP, and AI model behavior, then move intoapplying RAG pipelines, vector search, and prompting patterns. Finally, you'll tackleadvanced topics such asagentic systems, multi-agent orchestration, LangGraph workflows, MCP, and model fine-tuning.
Learn todesign and implement intelligent AI workflows and system components using multiple LLMs, LangChain, LangGraph, embeddings, and agentic reasoning—without the pressure of building full production applications.
Skip the beginner fluff—this isfor engineers, architects, and technical founders who want to understandhow modern GenAI systems are actually structured and engineered.
What You Will Learn
✨ Understand Generative AI foundations and how LLMs work, including OpenAI, Claude, Gemini, and Hugging Face models.
✨ ApplyRAG pipelines, vector search, embeddings, and structured outputs to create robust AI workflows.
✨ Learnprompting techniques, in-context learning, and fine-tuning strategies for advanced LLM behavior.
✨ Build and testagentic and multi-agent systems using LangChain and LangGraph.
✨ ExploreMCP servers and clients to integrate LLM reasoning with external tools and services.
✨ Understandsystem-level best practices for efficiency, scalability, cost, and responsible AI deployment.
Hands-On Learning
This is alearning-by-doing course, focused onframeworks, patterns, and exercises, rather than fully functional apps. You will
✨ Work withmultiple LLMs and open-source models to understand their behavior.
✨ Implementretrieval pipelines, multi-agent patterns, and workflows in hands-on exercises.
✨ ExploreLangChain, LangGraph, embeddings, vector databases, and MCP integration in manageable components.
✨ Gainpractical, reusable code snippets and exercises without the stress of shipping a full product.
Who this course is for
⭐ Software engineers and application developers who want to build real-world Generative AI, RAG, and agent-based systems.
⭐ Solution and platform architects designing LLM-powered products, workflows, and AI-first application architectures.
⭐ IT Professionals: Individuals aiming to transition into the growing field of Generative AI by gaining foundational knowledge and practical skills in AI-powered application development.
⭐ Startup builders and technical founders building AI-native products.
⭐ Professionals preparing for Generative AI Engineer, Applied AI, or Generative AI Architect roles.
Homepage
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