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.
better rag with late interaction free talk
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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.
I’ve been working on extracting data from documents, and I've shared a bunch to of stuff on the topic Hosted talks on OCR and VLMs to get tables, text, images, etc. out of documents Mini examples of which models and pipelines work in specific cases Retrieval on PDF documents to grab specific source data Antaripa Saha worked on #3 as part of her work in the AI Product Engineering Club. She compared keyword search, dense OCR, visual embeddings, multivectors, and reranking on complex financial...
Hey, I often see people struggling because users have different preferences and it's had to get what exactly they want out of them. This leads the agent (and your product) to give an answer that isn't what the user wants. I gave a talk and did a writeup on query disambiguation in the community. It covers patterns from AnkiHub, AI2 Paper Finder, NotebookLM, Deep Research, Censys, Lovable, Spiral, and Codex. Read or watch the post on the web. Isaac Query Disambiguation Read or Watch the talk on...
Hey, How to get data out of documents is the most common thing I get asked about privately, because if the source data extraction is wrong everything in the product will be wrong downstream. There's so many edge cases related to the reading order, tables, images, images with text inside them, scans, handwriting that is a picture embedded in a scanned document, etc. I hosted Joe Barrow for a deep dive on VLMs (vision language models) for OCR. He has processed hundreds of millions of document...