Building AI Agents & Chatbots with Python: LangChain, LLMs, RAG & Generative AI Workflows

Overview

🕮 Off the shelf (OTS)

1) Target Audience
This course is designed for developers, backend engineers, data engineers, and ML practitioners who want to build production-grade AI agents and LLM-powered applications using Python. It is also suited to technical founders and product builders creating AI-driven solutions.

2) Pre-requisites
A working knowledge of Python is beneficial but not essential. No prior experience with large language models or AI agent frameworks is required.

3) Course Description
This hands-on course takes delegates from Python and large language model (LLM) fundamentals through to building and deploying production-grade AI agents. The course covers how to design intelligent systems using tools, memory, retrieval-augmented generation (RAG), multimodal inputs, and multi-agent architectures — providing both the architectural understanding and practical implementation skills needed for real-world deployment.

4) Objectives / Topic Areas
• Python fundamentals for AI development
• Building and configuring an agent core loop
• Working with large language models via APIs
• Creating conversational AI and multimodal applications
• Building Model Control Protocol (MCP) servers
• RAG fundamentals and retrieval strategies
• Designing multi-agent systems
• Implementing agent memory and structured workflows
• Production considerations: monitoring, error handling, and deployment
• Using LangChain with Python for agent orchestration
• Capstone project: building a complete end-to-end AI agent

5) Delivery Format
Delivered over three days (21 hours in total, 7 hours per day) via virtual instructor-led sessions using Microsoft Teams or Zoom, with live demonstrations and hands-on exercises throughout.