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Vector search is an effective way to retrieve passages that are semantically similar to a question. It is not a universal interface to…Continue reading on Medium »
A field note on why your RAG app doesn't have a model problem — it has a retrieval problem. Built on a 346-page scanned Kannada novel: OCR, hybrid retrieval, reranking, determini...
RAG does not teach a model anything. It finds the right passages and pastes them into the prompt, which means the search is doing the work. That is why most RAG systems fail at ret...
Retrieval Is Not Search — What Actually Happens After You Hit similarity_search()Continue reading on Medium »
Retrieval-Augmented Generation (RAG) has become one of the most common approaches for building AI applications that need to work with external or private knowledge. Instead of aski...
Short answer: start a private fintech knowledge-base feature with embeddings, in-app retrieval, and grounded chat completions; keep reranking optional until real questions show tha...
RAG projects have a way of collecting infrastructure before they collect evidence. A database gets provisioned. A vector store appears. Then Redis, object storage, a parser servic...
Here's a dirty secret of search: "the closest match" and "the most useful result" are not the same thing. Return the mathematically nearest document and you'll often hand someone s...
Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline wi...
Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline wi...
This post focuses on why classic retrieval falls short on multi-part questions, how the AgenticRetrieveStream API works (including request construction and trace parsing), and when...
Most RAG tutorials focus on relevance — chunking strategies, embedding models, and hybrid search fusion. What they rarely address is security. In production, retrieval pipelines pu...
In this article, you will learn the conceptual and practical differences between retrieval and memory in agentic AI systems, and how to combine both effectively....
Medical records retrieval is the process of requesting, tracking, and collecting a client’s medical records from every provider involved in their care — hospitals, physicians, imag...
Medical records retrieval is the process of requesting, tracking, and collecting a client’s medical records from every provider involved in their care — hospitals, physicians, imag...
I have a confession, and I suspect I'm not alone in it: I've built RAG infrastructure multiple times, and until last week I had never benchmarked any of it. Unit tests, sure. Inte...
Move beyond traditional text-based parsing with PixelRAG, an end-to-end system that treats web pages and PDFs as images. This tutorial explores the complete pipeline—from renderi...
Enterprise Document Intelligence [Vol.1 #14D] - The index lists what the case type demands before any folder is opened, and the two questions worth building for are not retrieval q...
The MotivationMore and more enterprises are now asking agents to work with their...
Part 1 of a 3-part series on building retrieval-augmented generation for two very different environments. Two seconds is nothing. On a search page, you can spend two seconds look...
A model that has never seen your documents will answer questions about them anyway, confidently and wrongly. RAG fixes that by finding the right page first and handing it over with...
Challenging Search Terms and Scope of Discovery with Data Visualizationby Everlaw .....
Chunking decides more of your retrieval quality than the model does, and the obvious way to measure it rewards the worst possible answer. Here are eight strategies, measured on the...
The first three parts of this series covered why production RAG systems fail and how the quality of the data foundation directly affects everything that comes after it. We looked a...
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