🚀
10 Days Realtime LLM Bootcamp
  • Introduction
    • Getting Started
    • Course Syllabus
    • Course Structure
    • Prerequisites
    • Greetings from your Instructors
    • First Exercise (Ungraded)
  • Basics of LLM
    • What is Generative AI?
    • What is a Large Language Model?
    • Advantages and Applications of Large Language Models
    • Bonus Resource: Multimodal LLMs
  • 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 and Further Reads (Bonus Module)
    • Let's Track Our Progress
  • Prompt Engineering
    • What is Prompt Engineering
    • Prompt Engineering and In-context Learning
    • Best Practices to Follow in Prompt Engineering
    • Token Limits in Prompts
    • Prompt Engineering Excercise
      • Story for the Excercise: The eSports Enigma
      • Tasks in the Excercise
  • Retrieval Augmented Generation and LLM Architecture
    • What is Retrieval Augmented Generation (RAG)?
    • Primer to RAG Functioning and LLM Architecture: 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)
    • Track your Progress
  • Hands-on Development
    • Prerequisites
    • Dropbox Retrieval App in 15 Minutes
      • Building the app without Dockerization
      • Understanding Docker
      • Using Docker to Build the App
    • Amazon Discounts App
      • How the Project Works
      • Step-by-Step Process
    • How to Run the Examples
  • Live Interactions with Jan Chorowski and Adrian Kosowski | Bonus Resource
  • Final Project + Giveaways
    • Prizes and Giveaways
    • Tracks for Submission
    • Final Submission
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  1. Introduction

First Exercise (Ungraded)

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Last updated 1 year ago

To make this course interactive and engaging, let's get started with a small optional exercise.

  • Star the GitHub Repo:

    • We invite you to visit the and the and give it a star if you like the work. Doing so will show your support and keep you updated as the project evolves.

  • Join community:

    • Join your peers and mentors as we embark on this short yet exciting learning journey. We encourage you to introduce yourself in the #introductions channel, fostering a more personalized community experience. With the presence of some of the world's leading AI and Data researchers, you're in good company.

    • Additionally, as a best practice, we advocate for active participation in open-source communities like that of Pathway. Engaging proactively not only accelerates your learning but also opens doors to valuable connections and collaborations.

    • When introducing yourself, consider sharing any prior engagements in Artificial Intelligence, Data Engineering, or Academic Research. For those just beginning, feel free to discuss your existing background and how you aim to harness the potential of real-time LLMs. 😊

Ready to kickstart your LLM journey? The adventure awaits! 🚀

Pathway repository
LLM App GitHub repository
Pathway’s Discord