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3 Week Bootcamp: Building Realtime LLM Application
  • Introduction
    • Timelines and Structure
    • Course Syllabus
    • Meet your Instructors
    • Action Items
  • Basics of LLM
    • What is Generative AI?
    • What is a Large Language Model?
    • Advantages and Applications of Large Language Models
    • Bonus Resource: Multimodal LLMs and Google Gemini
  • Word Vectors Simplified
    • What is a Word Vector
    • Word Vector Relationships
    • Role of Context in LLMs
    • Transforming Vectors into LLM Responses
      • Neural Networks and Transformers (Bonus Module)
      • Attention and Transformers (Bonus Module)
      • Multi-Head Attention, Transformers Architecture, and Further Reads (Bonus Module)
    • Graded Quiz 1
  • Prompt Engineering
    • What is Prompt Engineering
    • Prompt Engineering and In-context Learning
    • Best Practices to Follow in Prompt Engineering
    • Token Limits in Prompts
    • Ungraded Prompt Engineering Excercise
      • Story for the Excercise: The eSports Enigma
      • Your Task
  • Retrieval Augmented Generation and LLM Architecture
    • What is Retrieval Augmented Generation (RAG)?
    • Primer to RAG: Pre-Trained and Fine-Tuned LLMs
    • In-Context Learning
    • High-level LLM Architecture Components for In-context Learning
    • Diving Deeper: LLM Architecture Components
    • LLM Architecture Diagram and Various Steps
    • RAG versus Fine-Tuning and Prompt Engineering
    • Versatility and Efficiency in Retrieval-Augmented Generation (RAG)
    • Key Benefits of RAG for Enterprise-Grade LLM Applications
    • Similarity Search in Vectors (Bonus Module)
    • Using kNN and LSH to Enhance Similarity Search in Vector Embeddings (Bonus Module)
    • Graded Quiz 2
  • Hands-on Development
    • Prerequisites
    • Dropbox Retrieval App in 15 Minutes
      • Building the app without Dockerization
      • Understanding Docker
      • Building the Dockerized App
    • Amazon Discounts App
      • How the Project Works
      • Repository Walkthrough
    • How to Run 'Examples'
  • Bonus Resource: Recorded Interactions from the Archives
  • Bootcamp Keynote Session on Vision Transformers
  • Final Project + Giveaways
    • Prizes and Giveaways
    • Tracks for Submission
    • Final Submission
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  1. Retrieval Augmented Generation and LLM Architecture

What is Retrieval Augmented Generation (RAG)?

PreviousRetrieval Augmented Generation and LLM ArchitectureNextPrimer to RAG: Pre-Trained and Fine-Tuned LLMs

Last updated 1 year ago

Large Language Models (LLMs) like GPT-4 or Mistral-7b are extraordinary in many ways, yet they come with a set of challenges.

For now, let's focus on one specific limitation: the timeliness of their data. Since these models are trained up to a particular cut-off date, they aren't well-suited for real-time or organization-specific information.

Imagine you're a developer architecting an LLM-enabled app for Amazon. You're aiming to support shoppers as they comb through Amazon for the latest deals on sneakers. Naturally, you want to furnish them with the most current offers available. After all, nobody wants to rely on outdated information, and the same holds for data queried from your LLM.

This is where Retrieval-Augmented Generation, commonly known as RAG, significantly improves the capabilities of LLMs.

In a way that might resemble a resourceful friend in an exam setting or during a speech who—figuratively speaking, of course—swiftly passes you the most relevant "cue card" out of a ton of information to help you understand what you should be writing or saying next.

With RAG, efficient retrieval of the most relevant data for your use case ensures the text generated is both current and substantiated.

RAG, as its name indicates, operates through a three-fold process:

  • Retrieval: It sources pertinent information.

  • Augmentation: This information is then added to the model's initial input.

  • Generation: Finally, the LLM utilizes this augmented input to create an informed output.

To put it simply, RAG empowers LLMs to include real-time, reliable data from external databases in their generated text.

For a better explanation, check out this video by Marina Danilevsky, Senior Research Staff Member at IBM Research where she shares two challenges with LLMs that are resolved with the help of Retrieval Augmented Generation.

(Credits: IBM Technology)

Perhaps that wasn't the perfect example, but you get the point.
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