Retrieval-Augmented Generation

Retrieval-Augmented Generation is a technique using prior data and/or content, ingesting that data into a specialized Database, most likely a Vector Databases or a Multi Modal Database, and then feeding it into an AI Model, such as an LLM. RAG promises to solve for the issue that most people and organizations are trying to work with AI in a way that is specific.

Building Production-Ready RAG Applications: Jerry Liu

Building RAG with enterprise open source AI infrastructure from Canonical

The BEST Way to Chunk Text for RAG

https://youtu.be/T-D1OfcDW1M?si=p-hYEfvkLU81j3xd

https://youtu.be/PLuSfAkOHOA?si=cK2wvPdSuJWvutDg

https://www.youtube.com/live/rL1wlYIyJho?si=ULpPcD2SQNIyR3RQ https://youtu.be/YAiEM59mpZc?si=-THqYA-MLlte5u6B

https://youtu.be/v5c5FV9cLAw?si=mHWaQ1qyDHkNztUJ https://youtube.com/shorts/xS55duPS-Pw?si=Vtlbl1tahjY-lKal

2025, February 2. [You HAVE to Try Agentic RAG with DeepSeek R1 (Insane Results)](https://youtu.be/uWDocIoiaXE?si=hACxzxH4qNI6Ez6z). Cole Medin. [[Deepseek]]