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Why the next serious AI systems will retrieve, reason, verify, and only then answer. Continue reading on Medium »

Enterprise Document Intelligence [Vol.1 #4] - A diagnostic across PDFs and questions, and a map of the techniques the rest of the series will cover The post Fro

We added web search to a local RAG pipeline. It looked straightforward. It wasn’t. What followed: broken assumptions about scoring a safety model that behaved d

Most people treat the RAG-vs-Agent question as a technical preference. Pick whichever feels right, adjust later. I did that. It cost me two full rebuilds. Here'

Introduction: The Perfection Trap and Side Project Realities This morning, while sitting at my computer, I was reviewing the logs of the RAG (Retrieval-Augmente

-Rethinking Retrieval Without Embeddings Continue reading on Medium »

GraphRAG is not just another AI buzzword. It is part of a larger architectural shift happening inside retrieval-augmented generation systems. Most early RAG sys

Most RAG systems are optimized for answer quality, not cost—and that blind spot gets expensive fast. In this article, I break down a production-ready cost contr

My data engineering background made me confident going into RAG. That confidence lasted about two weeks. Continue reading on Medium »

Not every GraphRAG question needs the same retrieval pattern. Some questions need the neighborhood around an entity. Some need a summary across a large part of

Moving beyond the “embed, retrieve, generate” demo every component you need to ship a reliable, observable, enterprise-ready RAG system. Continue reading on Med

Why Your RAG Pipeline Is Reasoning Over Broken Data. Continue reading on Medium »

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What is Embedding? After text is split into chunks, the next process is called embedding. In this step, each chunk is converted into vectors (points in vector s

Llevo meses trabajando en algo que empezó como una necesidad práctica y se ha convertido en la base de mi estrategia para entrar en el mundo del Red Team: un si

Cutting a long document into fixed-size chunks looks harmless until the most important sentence lands exactly on the boundary. Continue reading on Medium »

Sliding Window Chunking Sliding Window Chunking is a more intensive chunking mechanism. In this method, a window size is defined based on a character or token l

Most RAG demos look safe because they are small. Continue reading on Google Cloud - Community »

How I designed and built a retrieval-augmented generation system that ingests from Document360 and SharePoint, stores embeddings in Aurora… Continue reading on

The question that broke my RAG pipeline I had a solid RAG setup. Embeddings, vector store, top-k retrieval, the whole thing. It handled factual lookups just fin

Introduction FarmaStock AI is an academic Generative AI project focused on a real-world problem: how to make pharmacy stock analysis easier to understand and ex

A practical guide to Retrieval-Augmented Generation — the architecture pattern powering enterprise AI, coding agents, and the next… Continue reading on Jonathan

I audited a RAG pipeline at work last month. 43% of the retrieval calls were for questions the model already knew the answer to. Another… Continue reading on Ar

TL;DR : For RAG pipelines in 2026, pick pdfmux if you need free, local, benchmark-proven extraction with per-page confidence scoring (0.905 on opendataloader-be