Generative AI Tutorial – Slide 28

Understanding Retrieval‑Augmented Generation (RAG): How Generative AI uses external knowledge sources for better accuracy and reliability.

Slide 28 Image

Overview

Slide 28 introduces Retrieval‑Augmented Generation (RAG), a method that improves generative AI performance by combining a language model with an external knowledge retrieval system. Instead of relying solely on the model’s internal parameters, RAG enriches prompts with real, up‑to‑date, or domain‑specific data before generating a final response.

Key Concepts

Retrieval

Searches vector databases or documents for relevant context based on the user query.

Augmentation

Injects retrieved information into the prompt to provide grounding and factual support.

Generation

The LLM produces a final output using both the original prompt and retrieved knowledge.

How RAG Works

1

User Query

The user asks a question or sends a prompt.

2

Embedding + Search

The system converts the query to a vector and searches a knowledge store.

3

Context Retrieval

Relevant documents, snippets, or facts are fetched.

4

Enhanced Generation

The model generates a grounded and accurate response.

Applications of RAG

Enterprise Knowledge Assistants

Answering questions using internal documents, HR policies, manuals, or product databases.

Customer Support Bots

Generating accurate responses using support articles and FAQs.

Healthcare & Research

Providing insights grounded in medical studies or scientific databases.

Legal and Compliance Tools

Producing grounded summaries using statutes, regulations, or case law repositories.

RAG vs Basic LLM Generation

Traditional LLM

  • Uses only internal learned knowledge
  • Can hallucinate or generate outdated information
  • No direct access to proprietary or dynamic data

RAG‑Enhanced LLM

  • Grounded in real documents and updated knowledge
  • Reduces hallucinations dramatically
  • Customizable for specific domains through curated databases

FAQ

Does RAG replace fine‑tuning?

No. RAG complements fine‑tuning. Fine‑tuning teaches patterns, but RAG injects fresh, specific knowledge.

What data sources can RAG use?

PDFs, websites, databases, knowledge bases, FAQs, product catalogs—anything text‑convertible.

Is RAG expensive?

It depends on retrieval infrastructure size, but it often reduces LLM compute by improving accuracy first‑try.

Want to Learn More About Generative AI?

Continue exploring advanced topics like vector databases, embeddings, and model fine‑tuning.

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