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LLMs and GenAI in Digital Scholarship

An overview of how to use LLMs and GenAI in research and instruction.

RAG Tutorials

RAG Fundamentals and Advanced Techniques – Full Course
This comprehensive video tutorial provides a deep dive into the fundamentals and advanced techniques of Retrieval-Augmented Generation (RAG). It walks through the theory behind RAG and how to integrate it into machine learning workflows. The course is designed for those who want to understand the practical applications and nuances of RAG in AI projects.

Learn RAG From Scratch – Python AI Tutorial from a LangChain Engineer
This tutorial, presented by a LangChain engineer, offers a step-by-step guide to implementing RAG using Python. It begins with the basics and moves on to more complex implementations, giving viewers the tools to integrate RAG into their own projects with LangChain. The tutorial emphasizes hands-on examples and Python-based implementation.

deepset-ai/haystack-tutorial
This GitHub repository offers a series of tutorials on how to use Haystack, an open-source framework for building search systems that leverage RAG. The tutorials cover setting up Haystack, integrating different data sources, and optimizing RAG systems for production environments. This resource is ideal for those who prefer working through detailed documentation and code examples.

How to Implement RAG locally using LM Studio and AnythingLLM
In this tutorial, Fahd Mirza demonstrates how to set up RAG locally using LM Studio and AnythingLLM. It guides users through the configuration process, highlighting the steps necessary to run a local RAG system. This video is perfect for those who want to work with RAG outside of cloud-based solutions and set up their own local environment.

RAG is an evolving field with many options depending on your research goals, and on the tools and environments you wish to work in. Each of these tutorials showcases different platforms and techniques, providing a broad range of approaches. For additional resources or for more tailored guidance and assistance, feel free to reach out to the Freedman Center at FreedmanCenter@case.edu.