💪
3 Week Bootcamp: Building Realtime LLM Application
  • Introduction
    • Timelines and Structure
    • Course Syllabus
    • Meet your Instructors
    • Action Items
  • Basics of LLM
    • What is Generative AI?
    • What is a Large Language Model?
    • Advantages and Applications of Large Language Models
    • 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
      • Neural Networks and Transformers (Bonus Module)
      • Attention and Transformers (Bonus Module)
      • Multi-Head Attention, Transformers Architecture, and Further Reads (Bonus Module)
    • Graded Quiz 1
  • Prompt Engineering
    • What is Prompt Engineering
    • Prompt Engineering and In-context Learning
    • Best Practices to Follow in Prompt Engineering
    • Token Limits in Prompts
    • Ungraded Prompt Engineering Excercise
      • Story for the Excercise: The eSports Enigma
      • Your Task
  • Retrieval Augmented Generation 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
    • 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)
    • Graded Quiz 2
  • Hands-on Development
    • Prerequisites
    • Dropbox Retrieval App in 15 Minutes
      • Building the app without Dockerization
      • Understanding Docker
      • Building the Dockerized App
    • Amazon Discounts App
      • How the Project Works
      • Repository Walkthrough
    • How to Run 'Examples'
  • Bonus Resource: Recorded Interactions from the Archives
  • Bootcamp Keynote Session on Vision Transformers
  • Final Project + Giveaways
    • Prizes and Giveaways
    • Tracks for Submission
    • Final Submission
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Last updated 1 year ago

Welcome to the Final Stretch of Your Bootcamp Journey!

As we approach the conclusion of this bootcamp, it's time to transform your acquired knowledge into practical applications. The current deadline for submitting the project is 1st January 2023 (Monday). With two weekends ahead in this schedule, you have a valuable yet concise time frame to create and showcase a meaningful project. Make the most of it!

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

1 - Complete the Quizzes

  • Ensure you complete the required quizzes: one in the Vector Embeddings module and another in the RAG module.

2 - Project Development

  • Task: Develop a real-time or static RAG-based LLM application using the LLM App.

  • Publish: Publish your open-source project on your GitHub.

  • Submission: Submit the project link through the form provided.

Project Guidelines

  • Option to Modify an Existing Project: If building an LLM application from scratch seems daunting, consider modifying the "Dropbox Retrieval App" example we discussed. Adapt it to create an application with significant business or social value. For inspiration, look at how project for a better comprehension of the EU AI Act. This being said a replica of any published project will not be accepted.

  • Project Requirements:

    • Data Source: Your project should use real-time (preferred) or static data sources.

    • Open Source: Ensure your project is open source, hosted on GitHub with a clear README.md file and a License file as a best practice. Ref: / .

    • Documentation: The README.md must include:

      • A demo video link or GIF for a quick overview of your application.

      • A clear description explaining the purpose of your project and how it utilizes the .

      • Instructions for setting up and running the tool.

  • Originality: Your project must be original, not plagiarized, and not a direct replica of any course materials, publicly available projects, or those submitted by peers.

Encouragement for Innovation

  • While using the Dropbox App as a foundation is acceptable, we encourage you to innovate and create something unique. Challenge yourself to develop a project that tests your cognitive abilities and engineering skills.

Before you proceed, ensure you have registered.

To qualify for your bootcamp certificate, complete the required quizzes—one in the and another in the .

Concurrently, you're expected to build a real-time or static, RAG-based LLM application using the . While doing so, you have to make sure to publish your open-source project on GitHub and submit its link through the form ahead.

If the idea of creating an LLM application from the ground up (like the one we saw in the ) feels overwhelming, you have the option to build upon the "Dropbox Retrieval App" example discussed earlier. By tailoring it to meet specific needs, you can construct an application that holds substantial business or social value.

What are additional incentives beyond learning for building a novel application? Let's check out.

If you haven't registered yet, now is a good time to do so. Register here: .

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🎉
✌️
Richard adopted the Dropbox AI
Adding a License to a Repository
Tutorial for adding MIT License
LLM App
Vector Embeddings module
RAG module
LLM App
Amazon Discounts case
https://lu.ma/llm23