better rag with late interaction free talk


Hey this is Isaac,

Hamel Husain and I are doing a free public talk on why better RAG starts with late interaction.

Most RAG still follows the "embed the chunks, embed the query, compare the vectors” approach. But top retrieval architectures follow a new approach. People do not upgrade because it requires a different setup and they think it’s only an incremental improvement. Companies like Nvidia, AWS, and Cursor have adopted it because it’s a big step up.

It’s not just benchmark games. A client struggled with poor search results as a major user pain point. They spent months optimizing each part of their search pipline: hybrid search, rerankers, dataset curation, chunking, labeling, and other experimentation (query rewriting, decomposition, model choice, etc.).

The client switched to a simpler late interaction search setup, which basically solved their search quality issues. I’ve seen this many times.

In this talk, I will walk through how late interaction is different, why it gives better results, and the engineering tricks that make it work in practice.

It's free. Link here: https://maven.com/p/2d7a1c/better-rag-retrieval-with-late-interaction

Hope to see you there,

Isaac

Isaac Flath

Every post comes from something I've done on a real project. AI tools, development approaches, how I actually build things. You're getting a curation of my taste, not takes on stuff I don't use. Subscribers also get extras: things that went wrong, how my thinking about AI is changing, hacky workflows I use every day, and the occasional personal update. Stuff I share with subscribers because it's a little too personal or unpolished to blast across the internet.

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