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TUTBB · 2026-07-03T14:25:02+0100

AI Engineering Bootcamp Apps, RAG, Agents & MCP
Published 7/2026
Created by Arjun Vaid, School of AI
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 91 Lectures ( 20h 52m ) | Size: 12.7 GB
Build AI apps, advanced RAG systems, autonomous agents, MCP tools, and production-ready solutions in 14 days.

What you'll learn

Build practical AI applications using Python, Streamlit, and Large Language Models.

Understand modern AI concepts including Generative AI, LLMs, tokens, prompts, context windows, and hallucinations.

Write effective prompts using roles, instructions, constraints, examples, and structured output formats.

Create a Prompt Engineering Playground to test, compare, and save reusable prompts.

Build an AI Resume Analyzer that reviews resumes, scores them, and suggests improvements.

Extract text from PDFs and documents for use in AI applications.

Build a PDF Chat Assistant using Retrieval-Augmented Generation, also known as RAG.

Understand embeddings, semantic search, document chunking, and vector databases.

Use ChromaDB as a local vector database for document search and retrieval.

Build an autonomous AI Research Agent that can plan, search, analyze, write, review, and save reports.

Create a multi-agent workflow with Planner, Researcher, Writer, Editor, and QA agents.

Package an AI application with Docker and prepare it for portfolio or deployment.

Apply responsible AI practices including privacy, accuracy, guardrails, and human oversight.

Create portfolio-ready AI projects suitable for GitHub, resumes, interviews, and demos.
Requirements

No advanced AI, machine learning, or data science background is required.

Basic Python knowledge is helpful, but the course is beginner-friendly and explains the code step by step.

Basic command line or terminal knowledge is helpful for running Python apps and installing packages.

Students should have a computer with internet access.

An OpenAI API key is optional. Students can also use Ollama to run local models where supported.

Basic familiarity with APIs, web apps, or software development is helpful, but not required.

No advanced math is required.

No prior experience with RAG, AI agents, vector databases, Streamlit, ChromaDB, or Docker is required. These topics are introduced from the ground up through hands-on labs.

Most importantly, students should be curious and ready to build practical AI projects step by step.
Description
Move beyond basic chatbot tutorials and learn how to build complete, practical, and production-readyAI applications in just 14 days.
AI Engineering Bootcamp: Apps, RAG, Agents & MCP is a hands-on, project-based course designed to take you from the foundations ofGenerative AI to advancedAI agents,Retrieval-Augmented Generation,Model Context Protocol,multi-agent systems, and production AI engineering.
You will begin by understanding howLarge Language Models, prompts, tokens, responses, and AI application architectures work. You will build your first terminal andStreamlit AI chatbot, create reusable LLM service layers, and learn how to connect Python applications to cloud-based or local AI models such asOpenAI andOllama.
The course then introduces practicalprompt engineering techniques. You will learn how to structure prompts using roles, tasks, context, rules, examples, and output formats. Through a hands-on prompt playground, you will experiment with reusable prompt templates and understand how better prompts lead to more reliable AI applications.
Next, you will build real-world projects such as anAI Resume Analyzer, aPDF Chat Assistant, and anAutonomous Research Agent. You will learn how to process documents, extract text, create embeddings, split content into chunks, store vectors, and performsemantic search using tools such asChromaDB.
You will explore both beginner and advancedRAG systems. Topics include vector databases, hybrid retrieval, reranking, metadata filtering, query transformation,Knowledge Graph RAG, andAgentic RAG. You will use these concepts to build an enterprise-readyAI Knowledge Assistant capable of answering questions from business documents and private data.
The course also provides a deep introduction toAI agent engineering. You will learn how agents combine LLMs, tools, memory, planning, workflows, and state to complete complex tasks. You will build autonomous agents that can research topics, generate reports, use external tools, interact with websites, and evaluate their own outputs.
You will also buildmulti-agent AI systems using orchestration patterns such as planner, researcher, analyst, writer, and reviewer. You will exploreLangGraph, agent communication, shared state, task delegation, and enterprise multi-agent architectures.
A major part of the course focuses on theModel Context Protocol, commonly known asMCP. You will learn how MCP allows AI applications to connect securely with tools, files, APIs, databases, and enterprise systems. You will build your ownMCP server and understand how to design multi-server MCP ecosystems.
Additional topics includebrowser agents,multimodal AI, vision models, voice AI, evaluation, monitoring, observability, guardrails, responsible AI, governance, deployment, and production reliability.
By the end of this course, you will have built a strong portfolio ofAI engineering projects, including chatbots, RAG applications, autonomous agents, browser automation systems, MCP tools, multi-agent workflows, and a complete production AI platform.
This course is ideal for Python developers, AI enthusiasts, software engineers, data professionals, students, and anyone who wants to become anAI Engineer,Generative AI Developer,RAG Developer, orAgentic AI Engineer through practical, hands-on learning.
Who this course is for

This course is for beginners and intermediate learners who want to build practical AI applications instead of only learning AI theory.

It is ideal for software developers who want to add AI, LLMs, RAG, and AI agents to their skill set.

It is also useful for students who want portfolio-ready AI projects for GitHub, resumes, internships, interviews, or job applications.

Analysts, managers, consultants, and business professionals who want to understand how AI applications are built will also benefit from this course.

Entrepreneurs and creators who want to prototype AI-powered products, productivity tools, research assistants, resume tools, document chatbots, or business assistants will find the projects practical and reusable.

This course is also a good fit for Python learners who want to move beyond basic scripts and start building real AI-powered applications with Streamlit, LLM APIs, RAG, ChromaDB, agents, and Docker.

This course is not designed for learners looking for deep machine learning theory, advanced mathematics, or model training from scratch. The focus is practical AI application development.
Homepage

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https://www.udemy.com/course/ai-engineering-bootcamp-apps-rag-agents-mcp

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AI Engineering Bootcamp Apps, RAG, Agents & MCP

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