Wednesday, 16 September 2026

Agentic AI Foundations: Build RAG & MCP Chatbots

Python Developer September 16, 2026 AI No comments

Introduction

Agentic AI Foundations: Build RAG & MCP Chatbots is a beginner-level Coursera course from LearnKartS, taught by LearnKartS and Nikhil Agarwal. It is part of the Full Stack Agentic AI Specialization and focuses on building practical AI applications using RAG, MCP, embeddings, tool calling, Angular, Node.js, OpenAI, and Google Gemini. Coursera currently lists it as recently updated in April 2026, with 4 modules and an estimated completion time of about 2 weeks at 10 hours per week.

The main focus of the course is to move beyond simple chatbot development and understand how AI systems can retrieve information, use external tools, and interact with applications and APIs.

Join Now: Agentic AI Foundations: Build RAG & MCP Chatbots

🤖 1. Understanding Agentic AI

Traditional AI applications generally respond to user prompts.

Agentic AI goes a step further by creating systems that can:

Understand a task
Retrieve relevant information
Use tools
Interact with APIs
Process information
Perform actions
Work through multi-step workflows

The course introduces the evolution from traditional chatbots toward agentic systems and explains why RAG and MCP are important building blocks for these applications.

📚 2. What Is RAG?

Retrieval-Augmented Generation, commonly called RAG, allows an AI application to retrieve relevant information before generating an answer.

Instead of depending only on what the language model already knows, the application can connect the model to an external knowledge source.

A typical RAG workflow looks like:

User Question → Retrieval → Relevant Information → AI Model → Response

This approach is useful for applications such as:

Document Q&A
Knowledge assistants
Customer-support bots
Internal company search
FAQ systems
Research assistants

🔗 3. Understanding MCP

Another major topic is Model Context Protocol (MCP).

MCP provides a structured way for AI applications to interact with external tools and resources.

The course introduces concepts such as:

MCP architecture
MCP servers
Tools
APIs
Tool calling
Backend integration

This is important because an AI application can move beyond simply generating text and interact with external systems.

🧠 4. RAG + MCP

One of the most interesting concepts in the course is combining RAG and MCP.

RAG can provide an AI system with relevant information, while MCP can provide access to tools and external capabilities.

Together, they can support applications that can:

Retrieve → Understand → Decide → Use Tools → Respond

This creates a more capable architecture than a basic question-and-answer chatbot.

🧩 5. Embeddings and Similarity Search

The course also introduces embeddings.

Embeddings represent information in a form that allows an application to compare the semantic similarity between pieces of text.

This is useful for finding information that is conceptually related even when the wording is different.

The course covers:

Creating embeddings
Query embeddings
Document embeddings
Similarity search
Ranking retrieved results
Preparing context for an AI model

These concepts form an important foundation for modern RAG systems.

💻 6. Development Environment

The course takes a practical development approach.

Students set up tools such as:

Node.js
VS Code
Angular
OpenAI API
Google Gemini API

The course also introduces AI-assisted development and shows how tools such as Gemini Code Assistant can be used during development.

🎨 7. Building an Angular AI Chat Interface

The frontend portion uses Angular.

Students build a functional chat interface and work with concepts such as:

Message interfaces
Chat services
HTTP requests
API integration
Error handling
Environment variables
Responsive UI
Auto-scrolling

This makes the course more than a theoretical introduction because learners actually connect the frontend with an AI backend.

🟢 8. Node.js Backend Development

The backend is built using Node.js and Express.

Students learn how to:

Create an Express server
Configure environment variables
Create API routes
Connect frontend and backend
Integrate AI models
Handle chat requests
Test APIs

This full-stack approach helps learners understand how an AI application works beyond the model itself.

✨ 9. OpenAI and Gemini Integration

The course provides hands-on experience with both OpenAI and Google Gemini integrations.

Students learn how to configure API access and connect language models to their applications.

This gives learners experience working with different AI providers instead of depending on a single model ecosystem.

🗂️ 10. Building a RAG Chatbot

The final module focuses heavily on implementing RAG systems.

Students work through a complete process involving:

Creating a local knowledge base
Loading documents
Generating embeddings
Retrieving relevant information
Ranking results
Preparing context
Connecting the context with an AI model
Testing the resulting Q&A system

The course also explores RAG implementations using both Gemini and OpenAI.

🛠️ 11. Tool Calling and Real-World APIs

The course also introduces practical tool-based workflows.

For example, learners work with backend APIs involving customers, orders, and a weather API.

This demonstrates an important transition:

AI that only answers questions → AI that can interact with external systems.

That is one of the core ideas behind agentic AI development.

📖 12. Course Structure

The course currently contains 4 modules:

Module 1 — Agentic AI Fundamentals

Introduces AI agents, RAG, MCP, architectures, workflows, and the transition from traditional chatbots to agentic systems.

Module 2 — Environment Setup & RAG Foundations

Covers Node.js, Angular, API setup, embeddings, retrieval, and the foundations of the chatbot.

Module 3 — Angular Frontend Development

Builds the frontend, connects APIs, creates the backend, and integrates Gemini.

Module 4 — Building RAG Chatbots

Implements RAG pipelines, embeddings, similarity search, OpenAI/Gemini integration, and tool-oriented backend workflows.

🎯 Skills You Can Learn

According to the current Coursera listing, the course covers skills including:

Agentic systems
Generative AI agents
Embeddings
Tool calling
Retrieval-Augmented Generation
LLM applications
Node.js
Angular
OpenAI API
Google Gemini
Model Context Protocol
AI workflows

👨‍💻 Who Should Take This Course?

This course is particularly suitable for:

Beginner AI developers
Full-stack developers
JavaScript developers
AI/ML students
Generative AI learners
Developers interested in RAG
Developers interested in MCP
People building AI chatbots
Aspiring AI engineers

Basic programming knowledge will make the course easier to follow, especially because the implementation uses Angular and Node.js.

⭐ Strengths

✅ Practical Learning

The course focuses on actually building an AI application rather than only discussing concepts.

✅ RAG + MCP

It introduces two important technologies for modern agentic AI systems.

✅ Full-Stack Approach

Learners work with both frontend and backend components.

✅ Multiple AI Models

OpenAI and Gemini integrations provide exposure to different AI APIs.

✅ Real-World Workflow

The course moves from basic chatbot concepts toward retrieval, embeddings, APIs, and tool calling.

✅ Recently Updated

Coursera currently lists the course as updated in April 2026, making it relevant to the current Agentic AI development landscape.

⚠️ Things to Know Before Starting

Although the course is listed as beginner level, it is not a completely programming-free introduction to AI.

You will encounter:

Node.js
Angular
APIs
Backend development
Environment variables
Embeddings
RAG
MCP
AI model integration

So learners who already have some JavaScript or web-development experience may find the practical implementation easier.

Join Now: Agentic AI Foundations: Build RAG & MCP Chatbots

🏆 Final Verdict

Agentic AI Foundations: Build RAG & MCP Chatbots is a practical introduction to the technologies behind modern AI applications.

The course follows a useful progression:

Agentic AI → RAG → Embeddings → MCP → Tool Calling → Angular → Node.js → OpenAI/Gemini → AI Chatbot

Rather than stopping at prompt engineering, it introduces learners to the architecture required to build AI systems that can retrieve information and interact with external tools and services.