AI foundations
AI Fundamentals: LLMs, Agents and MCP
Learn generative AI and LLM fundamentals, prompting, agentic AI, retrieval-augmented generation and MCP.
A small sample of 30,000+ courses curated from around the world, all available to DoJo members.
A free 30-minute conversation with Ted about your goals.
Already a member? Open this course (opens in a new tab)
Course topics
AI foundations
- Language models
- Agentic RAG
- MCP fundamentals
You check the understanding
Find your fit
Is this a useful starting point?
For learners new to AI who have basic AWS and Git knowledge; programming familiarity is helpful.
- Starting point
- Beginner
- Course length
- 5 total hours
- Lessons
- 37 lectures
Course by Himanshu Rana
Course last updated: 03/2026
Requirements
- No prior experience required in AI, ML. We'll get that covered
- Basic programming knowledge (Python, yaml or JavaScript) can be helpful for hands-on examples
- Basic knowledge of AWS cloud and services like EC2, Lambda functions, cloudformation
- Basic knowledge of Git
- Curiosity to learn about AI
What is included
- 5 hours on-demand video
- 1 downloadable resource
- Access on mobile
- Certificate of completion
DoJo membership: A$250 per month per seat, excluding GST. Includes fortnightly group support and a private member community.
Explore the lessons
Course curriculum.
7 sections • 37 lectures • 5h 0m total length
Getting first hand taste of Generative Ai on your local Machine7 lectures • 31min
- Jumping right into the World of Artificial Intelligence02:01
- Setting up Ollama on Mac & Windows for local testing08:33
- Build your first Ai application: English Tutor using Ollama04:38
- Explore NotebookLM from Google04:54
- Course Roadmap03:15
- Know your Instructor - Skip if its boring01:21
- Taking a look at some of the widely used Closed and Open source models06:24
Fundamentals of Generative AI & LLMs5 lectures • 33min
- Generative AI vs traditional AI04:33
- What are Large Language Models (LLMs)06:00
- How LLM works09:20
- What are Tokens05:53
- AI Jargons - Inference, Context Window, hallucinations, RAG07:43
Art of Prompt Engineering: Understanding And Crafting good Prompts8 lectures • 36min
- Why need to engineer a prompt?03:20
- Role of Temperature, Top-K, Top-P in Sampling Output13:25
- Components of Prompt06:21
- Zero Shot Prompting02:20
- One-Shot or Few-Shot Prompting02:23
- Chain-of-Thought or CoT Prompting01:45
- ReAct or Reason and Act Prompting02:19
- System, contextual and role prompting03:50
Agentic AI Fundamentals and Development7 lectures • 1hr 7min
- What is an AI Agent04:22
- Components of AI agent05:35
- AWS Bedrock Agent - A Serverless Service05:40
- Understanding our AIOPS agent Architecture and Pre-req02:37
- Setup the AWS CLI and AWS Credentials10:25
- Create an AIOPS AI agent in AWS Bedrock29:52
- Responsible AI Principles08:21
Understanding and Implementing Agentic RAG5 lectures • 1hr 25min
- Understanding Retrieval Augmented Generation (RAG)13:12
- Front Desk Agent Without RAG - Understanding the problem13:33
- Implementing RAG based Front Desk agent using Langflow24:47
- Traditional RAG vs Agentic RAG11:07
- Implementing Agentic RAG22:08
Fundamentals of Model Context Protocol (MCP) and Use4 lectures • 44min
- What is MCP – The Universal Connector for AI03:52
- Get the taste of Docker MCP server10:06
- Basic MCP Architecture08:49
- Build your own MCP server to manage Your Google Calendar21:15
Conclusion1 lecture • 4min
- Wrap-up04:11
From learning to practice
Try this next.
Compare a basic language-model response with a retrieval or tool-connected example from the course.
A useful next step
Choose where to start.
Talk through your goals in a free 30-minute discovery call with Ted. Bring a task you want to improve and the tools you already use.
Book a discovery callExplore DoJo membership