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RAG and LLM Bootcamp
  • Welcome to the Bootcamp
    • Course Structure
    • Course Syllabus and Timelines
    • Know your Educators
    • Action Items and Prerequisites
    • Kick-Off Session for the Bootcamp
  • Basics of LLMs
    • What is Generative AI?
    • What is a Large Language Model?
    • Advantages and Applications of LLMs
    • 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
    • Bonus: Overview of the Transformer Architecture
      • Attention Mechanism
      • Multi-Head Attention and Transformer Architecture
      • Vision Transformers (ViTs)
    • Bonus: Future of LLMs? | By Transformer Co-inventor
    • Graded Quiz 1
  • Prompt Engineering and Token Limits
    • What is Prompt Engineering
    • Prompt Engineering and In-context Learning
    • For Starters: Best Practices
    • Navigating Token Limits
    • Hallucinations in LLMs
    • Prompt Engineering Excercise (Ungraded)
      • Story for the Excercise: The eSports Enigma
      • Your Task fror the Module
  • RAG 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
    • Basic RAG Architecture with Key Components
    • RAG versus Fine-Tuning and Prompt Engineering
    • Versatility and Efficiency in RAG
    • Key Benefits of using RAG in an Enterprise/Production Setup
    • Hands-on Demo: Performing Similarity Search in Vectors (Bonus Module)
    • Using kNN and LSH to Enhance Similarity Search (Bonus Module)
    • Bonus Video: Implementing End-to-End RAG | 1-Hour Session
    • Graded Quiz 2
  • Hands-on Development
    • Prerequisites (Must)
    • Docker Basics
    • Your Hands-on RAG Journey
    • 1 – First RAG Pipeline
      • Building with Open AI
      • How it Works
      • Using Open AI Alternatives
      • RAG with Open Source and Running "Examples"
    • 2 – Amazon Discounts App
      • How the Project Works
      • Building the App
    • 3 – Private RAG with Mistral, Ollama and Pathway
      • Building a Private RAG project
      • (Bonus) Adaptive RAG Overview
    • 4 – Realtime RAG with LlamaIndex/Langchain and Pathway
      • Understand the Basics
      • Implementation with LlamaIndex and Langchain
  • Final Project + Giveaways
    • Prizes and Giveaways
    • Suggested Tracks for Ideation
    • Sample Projects and Additional Resources
    • Submit Project for Review
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On this page
  • Advantages of LLMs over Traditional Neural Networks
  • Domains of LLM Applications
  • Creating Novel Solutions with Real-time LLM Applications
  • Evolving Scope of LLMs
  • Bonus Resources
  1. Basics of LLMs

Advantages and Applications of LLMs

PreviousWhat is a Large Language Model?NextBonus Resource: Multimodal LLMs and Google Gemini

As future engineers and innovators, it's crucial for you to not only understand Large Language Models (LLMs) but also to envision and develop solutions that tackle significant social and business challenges using this technology. However, the potential impact areas of LLMs are rooted in several key advantages they offer.

Let's briefly explore these foundational benefits.

Advantages of LLMs over Traditional Neural Networks

  • Scale of Data: Training on extensive datasets enhances LLMs' context understanding, leading to more nuanced outputs. Kudos to the rise of deep learning and significant hardware advances (e.g., GPUs), of course.

  • Transfer Learning: Similar to learning different sports after knowing a few, LLMs apply knowledge across tasks without starting from scratch.

  • Contextual Understanding: They perceive larger text contexts, not just isolated words or sentences.

  • Multi-Tasking Capability: Capable of handling diverse NLP tasks, unlike specialized traditional networks.

Now, these advantages lead to various critical applications for you to know.

Domains of LLM Applications

  • Even if you're not part of a dedicated LLM research team, knowing the common domains where LLMs are applied is crucial. It gives insight into investors' priorities and the areas where LLMs can drive substantial value. One widespread use case is for efficient knowledge extraction from a vast corpus of data. But let's get a bit more specific around a few use cases.

  • Examples: , healthcare ( and diagnostics),(content creation), and the financial sector (, summarization of financial meetings). Feel free to Google along these lines, and you'll find a plethora of resources around every single domain.

Creating Novel Solutions with Real-time LLM Applications

  • Building a "real-time" system fundamentally revolves around processing streaming data—handling new information as it arrives and incrementally indexing it efficiently for LLMs.

  • Think of this as a continuous learning process for LLMs, similar to how we humans learn. As we delve deeper into the course, we'll explore the nuances of incremental indexing via bonus resources, but for now, picture it as a system that constantly evolves and adapts.

  • Combining Real-Time Data Processing with LLMs: This integration forms a powerful value chain, which you'll learn to master by the end of this bootcamp. This synergy is pivotal in developing impactful solutions.

Evolving Scope of LLMs

  • Multimodality in LLMs: Continuous advancements in LLM capabilities, such as those in Google Deepmind's Gemini project, are expanding LLM interactions beyond text to include video, audio, and images. This opens a realm of possibilities for more dynamic and integrated AI applications.

  • Expanding Domains of LLM Research: Research is progressing in areas like reducing hallucinations, enhancing automated decision-making levels, and ensuring safer LLM applications. Innovations are also being made in processing larger data inputs more efficiently, exploring new model architectures beyond transformers, improving real-time data indexing, and enhancing the user experience in LLM applications.

Bonus Resources

For a deeper dive into the expansive world of LLM applications, feel free to explore these bonus resources:

  • If you’re curious about the potential limitations of LLMs as well, don’t worry; we’ve got that covered towards the end of this course.

While the bonus resources across this course are provided to ignite your curiosity, for now, you simply need to grasp the basics of Large Language Models (LLMs) and their varied applications. This will prime you for a deeper understanding of the upcoming modules and help you fully appreciate their transformative potential.

Let's continue! 🌐

as a starting point.

Then, head over to this blog about using .

Customer service (chatbots)
drug discovery
creative writing
fraud detection
Nvidia article
LLM Applications in production