Every RAG Strategy Explained in 13 Minutes (No Fluff)

Retrieval Augmented Generation is THE way to give your AI agents the ability to search and leverage your documents and knowledge. But there are a million strategies for RAG out there - how do you know what is ideal for your use case? What even are all of the RAG strategies I can pick from? Should I combine some together?

I've got you covered with all of these questions in this video. Beware this is very informationally dense, but it'll get you started thinking about what strategies to apply to your use cases.

Also let me know in the comments which RAG strategies you want me to cover more on my channel!

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- If you want to master AI coding assistants and learn how to build systems for reliable and repeatable results, check out the new Agentic Coding Course in Dynamous:
https://dynamous.ai/agentic-coding-course

- Neon is the Postgres platform I used to showcase real RAG data: check it out here:
https://get.neon.com/jR4SxEE

- Here is the repo that outlines all the RAG strategies with examples:
https://github.com/coleam00/ottomator-agents/tree/main/all-rag-strategies

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00:00 - Intro: The Importance of RAG
01:08 - RAG Explained in 1 Minute
02:22 - Resources for Each RAG Strategy
02:51 - RAG Strategy #1 - Reranking
03:46 - RAG Strategy #2 - Agentic RAG (includes Hybrid Search)
04:42 - RAG Strategy #3 - Knowledge Graphs
05:33 - RAG Strategy #4 - Contextual Retrieval
06:26 - RAG Strategy #5 - Query Expansion
06:56 - RAG Strategy #6 - Multi-Query RAG
07:22 - RAG Strategy #7 - Context-Aware Chunking
08:20 - RAG Strategy #8 - Late Chunking
09:08 - RAG Strategy #9 - Hierarchical RAG (Using Metadata)
10:15 - RAG Strategy #10 - Self-Reflective RAG
10:51 - RAG Strategy #11 - Fine-tuned Embeddings
12:00 - Final Thoughts

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Join me as I push the limits of what is possible with AI. I'll be uploading videos weekly - at least every Wednesday at 7:00 PM CDT!