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Intermediate Linux • Basics of LLMs
skills learned
Create embeddings from a body of text • store embeddings in a vector database • build a RAG LLM application • create a Discord chatbot
Michael Yuan and Tony Yuan
1 week · 4-6 hours per week · BEGINNER

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Fine-tuning an LLM is time-consuming and expensive! That’s why your local school district has tasked you with using RAG (Retrieval Augmented Generation) to help improve the capabilities of a chemistry chatbot based on Meta AI’s Laama. RAG allows an LLM to search a database to help answer questions, avoiding any unfortunate hallucinations. You’ll create a vector database for external knowledge for your chatbot’s RAG, establish an RAG API server for it to use, and then deploy your new bot to both the web and Discord.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project authors

Michael Yuan

Dr. Michael Yuan is the founder and maintainer of the open -source WasmEdge project — one of the most popular WebAssembly (Wasm) runtimes for server and edge applications. He is also the creator of LlamaEdge, a cross-platform, embeddable, and cloud-native runtime for AI applications, supporting LLMs and popular vision and speech models. Dr. Yuan is the author of 5 books on software engineering and a frequent speaker at conferences such as KubeCon, Open Source Summit, QCon, Rust Conf, VMWare Explore, COSCon, and Wasm I/O. He has designed and implemented enterprise applications and server-side software infrastructure since the early 2000s. Dr. Yuan holds a PhD in Astrophysics from the University of Texas at Austin.

Tony Yuan
Tony Yuan is a student at Kealing Middle School in Austin, Texas, who loves technology and AI. He served as a TA for his school’s computer science class and as the leader of Kealing’s philharmonic orchestra. He enjoys listening to rock music and reading history books when he's not programming or studying.


This liveProject is for software developers and IT professionals who are interested in building LLM applications in their own domains. To begin this liveProject, you will need to know the following:

  • Intermediate Linux
  • Basics CMAKE and Linux dev tools
  • Basics of Qdrant vector database
  • Basics of Python
  • Command line tools
  • Basics of LLMs
  • Basics of model training and inference
  • Deploying and testing web services


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