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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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Final Project + Giveaways

Note: This module was specifically tailored for the live cohort during the bootcamp. If you're accessing this coursework after the bootcamp has concluded, it is not applicable for you.

PreviousLive Interactions with Jan Chorowski and Adrian Kosowski | Bonus ResourceNextPrizes and Giveaways

Last updated 1 year ago

Welcome to the Final Stretch of Your Bootcamp Journey!

As we approach the conclusion of this mini bootcamp, it's time to transform your acquired knowledge into practical applications.

To guide you, we've carefully selected a range of exciting project tracks. These projects are your platform for innovation and making a tangible impact! 🌟

How to Successfully Complete the Bootcamp

  • To qualify for your bootcamp certificate, complete the required MCQs—one in the Vector Embeddings module and another in the RAG module.

  • Concurrently, you're expected to build a real-time, RAG-based LLM application.

  • Suppose the idea of creating an LLM application from the ground up (like the one we saw in the Amazon Discounts case) feels overwhelming. In that case, you can build upon the "Dropbox Retrieval App" example discussed earlier by tailoring it to meet specific needs. For example, you can construct an application with substantial business or social value, like the EU AI Act showcase, which successfully repurposed the Dropbox Retrieval App example to simplify understanding complex legal documents in the AI domain.

  • That said, there are added incentives (beyond learning) to make a novel application using the LLM App.

What are those incentives? Let's read.

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