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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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On this page
  • What we'll be learning to get there:
  1. Introduction

Course Syllabus

PreviousGetting StartedNextCourse Structure

Last updated 1 year ago

By the end of this course, you will:

  • Be proficient in developing LLM-based applications for production applications from day 0.

  • Have a clear understanding of LLM architecture and pipeline.

  • Be able to perform prompt engineering to best use generative AI tools such as ChatGPT.

  • Create an open-source project on a real-time stream of data or static data.

What we'll be learning to get there:

Module
Topics
Module
Topics
Module
Topics
Module
Topics
Module
Topics

1 – Basics of LLMs

  • What is generative AI and how it's different

  • Understanding LLMs

  • Advantages and Common Industry Applications of LLMs

  • Bonus section: Google Gemini and Multimodal LLMs

2 – Word Vectors

  • What are word vectors and word-vector relationships

  • Role of context in LLMs

  • Transforming vectors in LLM responses

  • Bonus Resource: Overview of Transformers Architecture and Vision Transformers

3 – Prompt Engineering

  • Introduction and in-context learning

  • Best practices to follow: Few Shot Prompting and more

  • Token Limits

  • Prompt Engineering Peer Reviewed Exercise

4 – RAG and LLM Architecture

  • Introduction to RAG

  • LLM Architecture Used by Enterprises

  • Architecture Diagram and LLM Pipeline

  • RAG vs Fine-Tuning and Prompt Engineering

  • Key Benefits of RAG for Realtime Applications

  • Simialrity Search for Efficient Information Retrieval

  • Bonus Resource: Use of LSH + kNN and Incremental Indexing

5 – Hands-on Project

  • Installing Dependencies and Pre-requisites

  • Building a Dropbox RAG App using open-source

  • Building Realtime Discounted Products Fetcher for Amazon Users

  • Problem Statements for Projects

  • Project Submission