Course Description:
This module introduces the essential building blocks for connecting AI systems to the tools, services, and data they need to operate effectively. You’ll start by understanding tool calling, how large language models (LLMs) trigger external services to perform actions beyond simple text generation. We’ll explore common integration challenges and break down API basics in plain language for non-technical professionals.

Next, you’ll dive into Retrieval Augmented Generation (RAG), a powerful technique for enhancing AI with up-to-date, context-rich information. You’ll learn how to create simple document collections, feed them into your AI system, and implement RAG in straightforward scenarios without heavy coding.

By the end of this module, you’ll understand how to bridge the gap between AI models and real-world applications, enabling smarter, more relevant, and more connected AI solutions.

Lecture 1: Understanding Tool Calling

This lecture demystifies tool calling, showing how LLMs connect with external services to perform actions beyond text generation. You’ll also learn API fundamentals for non-technical users and how to overcome common integration challenges.

Key objectives:

• How LLMs invoke external services
• Common integration challenges
• API basics for non-technical users

Lecture 2: Introduction to RAG (Retrieval Augmented Generation)

This lecture introduces Retrieval Augmented Generation (RAG) and how it boosts AI with up-to-date, context-rich information. You’ll explore basic document collections and simple implementation examples to understand its practical business applications.

Key objectives:

• Enhancing AI with up-to-date data
• Basic document collections
• Simple implementation examples