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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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  1. Word Vectors, Simplified

Bonus: Future of LLMs? | By Transformer Co-inventor

PreviousVision Transformers (ViTs)NextGraded Quiz 1

Last updated 11 months ago

Okay, now that we briefly know how LLMs work and what transformer architecture is, let's address the elephant in the room. Currently, LLMs are becoming more powerful as they're trained on increasingly vast amounts of data. But what happens when the data available on the internet reaches its limit, becoming saturated and overrun by noise generated by AI itself? How will models continue to evolve then?

To tackle this question, we have Łukasz Kaiser at the Pathway Bay Area Meetup. Łukasz, a supporter of Pathway and a pivotal figure in the development of transformative AI technologies. He is the co-creator of TensorFlow, Transformer Architecture, ChatGPT, GPT-4o, and more.

Insights from Łukasz Kaiser

In his presentation titled "Deep Learning Past and Future: What Comes After GPT?", Łukasz explores the future trajectory of LLMs in an era of potential data scarcity. He discusses how the landscape of deep learning has evolved and what strategic shifts are necessary to sustain further advancements.

The Core of the Discussion

Łukasz highlights a critical shift from the traditional paradigm of 'more data, better results' to a new model of efficiency. He proposes that the next generation of LLMs will need to achieve greater sophistication not by simply consuming more data, but by using smarter, high-quality data sets. This includes leveraging techniques that enhance a model's ability to retrieve and interpret relevant information quickly and accurately.

Why This is Important

For anyone engaged with the development or application of AI, understanding these evolving strategies is crucial. Łukasz's insights provide a roadmap for how AI can continue to develop in a sustainable and effective manner, even as traditional resources become constrained.

The latter part of the video involves a talk by (CTO at Pathway), a former colleague of Łukasz Kaiser from Google Brain, around what are the best ways in which get better retrieval for better reinforcement learning of foundational LLMs. This part is best covered after we've understood RAG later in this course. Till then, let's wait and consolidate what we've already learned.

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Jan Chorowski