An Introduction to RAG: Retrieval-Augmented Generation

# Audience: Suitable for anyone, including those new to RAG

# What will be covered:
- A conceptual overview of RAG as a solution approach
- How to design a basic RAG architecture

# What will not be covered:
- In-depth source code or implementation details
- MCP (Model Context Protocol)

Transcript

1. Ray Tsou – June 16, 2025 An Introduction to RAG: Retrieval-Augmented Generation
2. If a RAG Application were a personal assistant… Who would it be?
3. Session Overview • Inclusions: • Gain a conceptual understanding of RAG as a solution approach • Learn how to design a basic RAG architecture
4. Agenda ɾApproaches to Enhancing generative AI Model Capabilities ɾIntroduction to Vectorization & Vector search ɾIntroduction to RAG ɾRAG Architecture Breakdown ɾAdditional RAG Application Ideas ɾKey Takeaways ɾQ&A
5. Approaches to Enhancing generative AI Model Capabilities Technique Cost Development Effort Data Freshness Performance Boost Use Case Fit Prompt Engineering Low Low Real-time possible Moderate Best for fast iterations, simple logic, or when you want to tweak behavior without backend changes. Fine-Tuning High High Static High Ideal when you need deep customization for a stable, well-de fi ned domain (e.g., legal, medical). RAG Medium Medium Real-time possible High Great when you need to incorporate live or external knowledge into the model’s answers (e.g., product info, document Q&A, internal tools).
6. Vectorization & Vector Search • Vectorization converts text into numerical vectors that represent its meaning. • Vector search fi nds similar meanings by comparing these vectors, rather than matching exact words. Vector search Vectorization
7. Introduction to RAG RAG is a technique for enhancing the accuracy and reliability of generative AI models with information from speci f ic and relevant data sources.
8. RAG Architecture Breakdown - 1
9. RAG Architecture Breakdown - 2
10. RAG Architecture Breakdown - 3
11. Now that you understand RAG, what applications could make work or life easier?
12. Additional RAG Application Ideas • Code and Documentation Retrieval • Store your code and design documents in a vector database. When errors happen, use error messages to retrieve relevant content, helping identify potential issues and solutions e ff i ciently. • Support Knowledge Base • Store historical records—including queries and solutions like end-user questions or system errors—in a vector database. This helps new team members quickly fi nd accurate responses. Use a relevance threshold to ensure only highly relevant documents are retrieved; if none qualify, reply that no relevant fi le is available. • Chatbot Assistant • Add RAG-powered chatbots to pipelines or Slack for instant, on-demand support.
13. If I just load a bunch of PDFs into a vector database, a RAG Application can answer your questions.
14. Key Takeaways RAG combines the strengths of retrieval and generation to produce more accurate, relevant, and context-aware responses. It’s an approach for building smarter, more informed AI systems.
15. Q&A