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Foundations of Generative AI and LLMs

Introduction to Generative AI and Large Language Models

How LLMs Work in Applications

Introduction to Retrieval-Augmented Generation (RAG)

The Basics of RAG Architecture

Enhancing AI Responses with RAG

Understanding Generative AI Agents

Introduction to AI Agents

Basic Agent Workflows and Tools

Tools and Frameworks for AI Engineering

Essential Frameworks for RAG and Agents

Data Storage and Vector Databases

Knowledge Checks

Quiz: Generative AI and LLM Foundations

Quiz: RAG Architecture Basics

Quiz: AI Agent Concepts

Quiz: Tools and Frameworks

Quiz: Comprehensive Review

Course Feedback

Course Evaluation Survey

Welcome to the "Foundations of Generative AI Application Engineering and RAG Systems" course, your comprehensive guide to mastering the core technologies driving the modern AI revolution. In today's rapidly evolving tech landscape, Large Language Models (LLMs) are transforming how businesses operate, but standalone models often suffer from limitations like hallucinations and outdated information.

Welcome to the "Foundations of Generative AI Application Engineering and RAG Systems" course, your comprehensive guide to mastering the core technologies driving the modern AI revolution. In today's rapidly evolving tech landscape, Large Language Models (LLMs) are transforming how businesses operate, but standalone models often suffer from limitations like hallucinations and outdated information. This course bridges the gap between basic AI concepts and robust, enterprise-ready application engineering. You will dive deep into the mechanics of Generative AI, exploring how LLMs function and why advanced engineering is crucial for reliability. We will demystify Retrieval-Augmented Generation (RAG), teaching you how to ground AI responses in factual, external data using embeddings and vector databases. Beyond RAG, you will explore the fascinating world of autonomous AI agents, learning how to design workflows that allow AI to use external tools, execute tasks, and make decisions. By leveraging industry-standard frameworks like LangChain and LlamaIndex, you will gain the foundational knowledge needed to architect intelligent applications. Whether you are a software developer, a data enthusiast, or a technical product manager, this course provides the essential building blocks to confidently integrate Generative AI into real-world software solutions. Join us to unlock the potential of LLMs, RAG architectures, and AI agents, and take your first step toward becoming a proficient AI application engineer.

Foundations of Generative AI Application Engineering and RAG Systems

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            <p>The rapid advancement of artificial intelligence has fundamentally transformed modern software development, placing Generative AI and Large Language Models (LLMs) at the forefront of technological innovation. This course provides a comprehensive exploration of the foundational principles required to engineer sophisticated, AI-driven applications. Learners will begin by examining the core mechanics of LLMs, including their text generation capabilities, common use cases, and inherent limitations such as hallucinations and knowledge cutoffs. By establishing a strong theoretical baseline, the curriculum illuminates why standalone models require additional engineering and strategic prompting to become reliable, enterprise-grade solutions.</p>

<p>Building upon these foundational concepts, the course delves into advanced architectural paradigms, specifically focusing on Retrieval-Augmented Generation (RAG) and autonomous AI agents. Participants will explore how RAG systems mitigate inaccuracies by grounding AI responses in factual, retrieved data, utilizing embeddings and specialized vector databases for efficient information retrieval. Furthermore, the curriculum distinguishes between standard LLM outputs and action-oriented AI agents, detailing workflows that incorporate external tools, system prompts, and APIs to execute complex tasks. To bridge theory and practice, the program also examines essential industry-standard frameworks, such as LangChain and LlamaIndex, equipping learners with the knowledge to seamlessly connect language models with external data sources and construct robust, intelligent applications.</p>
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