Inside multimodal RAG

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Course Overview:

Duration: 2 Hours

We'll walk through

- Building tools to find relevant reviews using vector embeddings and semantic search

- Implementing structured generation with Outlines to extract specific data points

- Applying structured data analysis problems

The goal of the course is to demonstrate how to turn unstructured data into reliable, structured data that you can actually use. We'll provide example code and schemas that you can adapt for your own projects. Basic Python and LLM experience recommended, but not required.

Instructor:

Cameron Pfiffer

Developer Relations Engineer | .txt

Cameron Pfiffer is a Developer Relations Engineer at .txt, where he works to educate and understand developers. His work focuses on demonstrating the power of reliable AI systems. Cameron has expertise in Retrieval-Augmented Generation (RAG), knowledge graph implementations, and structured generation approaches. His technical work emphasizes practical solutions to problems faced by AI engineers. Cameron comes from a non-traditional background, with a PhD in financial economics. He earned his PhD from the University of Oregon, where he studied Bayesian methods applied to asset pricing. Following his doctorate, he completed a postdoctoral position at Stanford University, focusing on computational methods for structural economics. Cameron is an advocate for open-source software, particularly scientific computing. He has contributed to projects in the generative AI space, probabilistic programming, web tooling, and other miscellaneous projects. He regularly shares insights through technical writing, presentations, and workshops.