🌉
3-Week Building LLMs Bootcamp
  • Welcome to the Bootcamp
    • Course Structure
    • Course Syllabus and Timelines
    • Know your Educators
    • Action Items and Prerequisites
    • Kick Off Session at Tryst 2024
  • 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
    • Group Session Recording
  • Word Vectors, Simplified
    • What is a Word Vector
    • Word Vector Relationships
    • Role of Context in LLMs
    • Transforming Vectors into LLM Responses
    • Bonus Section: Overview of the Transformers Architecture
      • Attention Mechanism
      • Multi-Head Attention and Transformers Architecture
      • Vision Transformers
    • Graded Quiz 1
    • Group Session Recording
  • Prompt Engineering and Token Limits
    • What is Prompt Engineering
    • Prompt Engineering and In-context Learning
    • For Starters: Best Practices to Follow
    • Navigating Token Limits
    • Hallucinations in LLMs
    • Prompt Engineering Excercise (Ungraded)
      • Story for the Excercise: The eSports Enigma
      • Your Task for the Module
    • Group Session Recording
  • 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
    • Group Session Recording
    • Graded Quiz 2
  • Hands-on Development
    • Prerequisites
    • 1 – Dropbox Retrieval App
      • Understanding Docker
      • Building the Dockerized App
      • Retrofitting your Dropbox app
    • 2 – Amazon Discounts App
      • How the Project Works
      • Building the App
    • 3 – RAG with Open Source and Running "Examples"
    • 4 (Bonus) – Realtime RAG with LlamaIndex/Langchain and Pathway
      • Understanding the Basics
      • Implementation with LlamaIndex and Langchain
    • Building LLM Apps with Open AI Alternatives using LiteLLM
  • Bonus Resource: Recorded Interactions from the Archives
  • Final Project + Giveaways
    • Prizes and Giveaways
    • Suggested Tracks for Ideation
    • Sample Projects and Additional Resources
    • Form for Submission
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  1. Final Project + Giveaways

Sample Projects and Additional Resources

PreviousSuggested Tracks for IdeationNextForm for Submission

Last updated 1 year ago

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  • 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.

  • If you're unable to use OpenAI models, you can use any other model from Coherence, Replicate, Gemini, etc. via . If you're going for open source / local models, resources are mentioned in the previous modules.

  • 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. 😊

  3. ()

  5. (uses Gemini)

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
Pathway Lite LLM Embedder
https://github.com/leabuende/mike-llm-slack-plugin/
https://github.com/abdul756/AURA
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