Chapter 5: Retrieval-Augmented Generation (RAG): Beyond Model Knowledge

Fri, 16 Jan 2026 00:00:00 +0000

Introduction to Retrieval-Augmented Generation (RAG)

Welcome back, future Applied AI Engineer! In the previous chapters, we laid a solid foundation in Python, system thinking, and started interacting with Large Language Models (LLMs) through APIs and prompt engineering. We learned how to guide LLMs with clever prompts and even give them tools to extend their capabilities. But what if an LLM doesn’t know about the latest company policies, your personal notes, or proprietary product documentation? That’s where its “knowledge cut-off” becomes a limitation.

Applied & Agentic AI: A Zero-to-Pro Career Path

Fri, 16 Jan 2026 00:00:00 +0000

Welcome to your definitive guide to becoming a professional Applied AI and Agentic AI Engineer! This learning path is meticulously crafted to take you from foundational programming principles to designing, building, and deploying sophisticated AI agents and intelligent systems, all with a strong emphasis on practical application and real-world problem-solving.

What is Applied AI and Agentic AI Development?

At its core, Applied AI is about bringing artificial intelligence out of the theoretical realm and into practical use, solving concrete business problems or enhancing existing applications. It’s about building solutions that leverage AI models (like Large Language Models, or LLMs) to perform specific tasks, automate processes, and provide intelligent capabilities.

Applied AI on AI VOID

Chapter 5: Retrieval-Augmented Generation (RAG): Beyond Model Knowledge

Introduction to Retrieval-Augmented Generation (RAG)

Applied & Agentic AI: A Zero-to-Pro Career Path

What is Applied AI and Agentic AI Development?