Module 5: Edge RAG Concepts

Overview

Explore Retrieval-Augmented Generation (RAG) fundamentals and learn how to deploy AI at the edge for sovereign environments.

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Module Content

📚 Main Content:

• Edge RAG Concepts - RAG fundamentals, edge deployment benefits, customer scenarios

Deep Dive Topics:

• RAG Fundamentals - Embeddings, vector databases, LLM limitations
• Edge RAG Architecture - Local LLM deployment, ingestion pipeline, query processing
• Edge RAG Use Cases - Industry scenarios, best practices, ROI

✅ Assessment:

• Knowledge Check - 15 questions testing RAG concepts and implementation

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Key Concepts

• RAG architecture and components
• Vector databases and embeddings
• Local LLM deployment and inference
• Data sovereignty for AI workloads
• Edge computing benefits
• Industry-specific use cases

Duration: 2-2.5 hours

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Table of contents

• Overview
• RAG Fundamentals
• Edge RAG Architecture
• Edge RAG Use Cases
• Edge RAG Knowledge Check