RAG & Vector Search

Last week we compressed retrieved chunks so the model receives only the sentences that matter. Continue reading on Operations Research Bit »

I tested HyDE Retrieval vs Standard vector search on semantic, proprietary, and keyword queries. HyDE won one and quietly made 2 worse. Continue reading on Towa

Practical GenAI patterns for connecting RAG systems with enterprise-scale data platforms Continue reading on Medium »

Enterprise Document Intelligence [Vol.1 #7A] - Stop searching strings. Filter line_df and toc_df. Pick anchors small, expand context large The post Retrieval Is

Enterprise Document Intelligence [Vol.1 #6bis] - Ask one focused clarification, learn the default from the answer, stay silent next time The post When RAG Users

Enterprise Document Intelligence [Vol.1 #5septies] - When a PDF prints a contents page but exposes no outline, two ways to turn it back into structure, plus the

I Spent $500 on RAG Infrastructure Before Realizing These 7 Mistakes Were Killing My Results I built a RAG pipeline for private document search. It cost me $500

Originally published at twarx.com - read the full interactive version there. Last Updated: June 20, 2026 Every RAG pipeline your team shipped in 2023 is silentl

Or: How I taught an LLM to read Wikipedia and answer questions without hallucinating Continue reading on Medium »

There is no one-size-fits-all “best” RAG pipeline for O-RAN; the ideal architecture depends entirely on your specific operational… Continue reading on Medium »

You asked your RAG app a question last week, got an answer that sounded confident and polished, and only later realized it was wrong in a… Continue reading on M

If your RAG app sometimes answers from the wrong document even though the right one was in your database, the fix usually isn't a better embedding model — it's

One thing that stands out while working with RAG systems is how much influence the retriever has on the final answer. A common assumption is that improving the

Artificial intelligence has moved from experimentation to execution. Businesses are using it to serve customers better, speed up… Continue reading on Medium »

Enterprise Document Intelligence [Vol.1 #5ter] - Table cells, OCR, captions, headings: cloud-grade structure, running on your own machine. No key, no per-page b

If you've read more than three RAG tutorials, you've absorbed the same advice I have. Use hybrid search, not just dense. Agonise over your chunking strategy. Re

Enterprise Document Intelligence [Vol.1 #5bis] - The same relational tables. Native table cells. OCR for scanned pages and images. Captions and headings without

Enterprise Document Intelligence [Vol.1 #5B] - One PDF in, a relational set of DataFrames out: lines, pages, TOC, images, cross-references, captions, spans, and

I Built a Production RAG System on My M1 Mac for $0 Most RAG tutorials stop at "it answers questions." But answering questions is table stakes. The real questio

Part 3 of 10, Becoming an AI Engineer Continue reading on Medium »

A practitioner’s observation from building enterprise RAG for solution architects Continue reading on Medium »

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

Artificial Intelligence has evolved rapidly, but one major limitation still exists: models don’t always know everything and sometimes they… Continue reading on

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When to use it, why it’s hard to ship, and how to make it reliable Continue reading on Data Science + AI at Microsoft »