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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. Final Project + Giveaways

Sample Projects and Additional Resources

PreviousSuggested Tracks for IdeationNextSubmit Project for Review

Last updated 10 months ago

  • A Google Sheet with Ideas and Data Sources: We're providing as an additional resource to fuel your creative thinking. We have also included a list of real-time data sources in the document.

  • Please be aware that these ideas originated from a weekend brainstorming session done months ago by one of the course instructors (Mudit) and are not fully developed solutions. As developers, you may encounter technical challenges that haven't been specifically outlined in the document. The document should primarily serve as a guide for ideation around business use-cases of RAG/LLMs.

  • For UI component, most of these projects have used , a popular Pythonic tool offered by Snowflake for data scientists. You can also use it.

  • Recently Developed Projects: This selection showcases projects created in the last 4-5 months by the Pathway community. Except for one, all the curated projects below are by student developers. 😊

  6. ()

LLM App Showcases: As seen , the LLM App templates repository contains a variety of use cases within its , showcasing its wide range of applications. We encourage you to revisit this module to gain a better understanding and to discover the many innovative applications developed using it."

For example, this link shares how you can build your LLM app using local models instead of going for hosted APIs: .

this Google Sheet
Streamlit
https://github.com/abdul756/AURA
https://github.com/leabuende/mike-llm-slack-plugin/
https://github.com/Alphawarrior21/AeroIntel.git
https://github.com/ananyaem/UPSC-LLM/
https://github.com/AnavAgrawal/AlgoAce
https://github.com/Paulescu/virtual-assistant-llm
Video
https://github.com/Arjun-G-04/github-ai
https://github.com/souvikcseiitk/gate_cse_gpt
https://github.com/SaumyaRR8/Youtube-playlist-chat
https://github.com/CodeAceKing382/Stocks-Insight-App
https://github.com/Sriraj-dev/VidQuest
https://github.com/TushnikaC/InquireMate
https://github.com/meghanmane84/Disaster-News-Alerts-RAG
https://github.com/atiabjobayer/transfinitte-team404
https://github.com/purrate/trail
https://github.com/atulkrishna-4100/AdsGPT_Pathway_project
https://github.com/AnimeshN/nutriGPT-database-python
earlier
examples folder
Link