RAG & Vector Search

Not all ghosts are scary — some just need a good library card.  Continue reading on CodeToDeploy »

Enterprise Document Intelligence [Vol.1 #8B] - A fixed BASE, the rules each question needs, one registry: the dispatcher that turns a parsed question into a typ

How to get accurate, source-backed answers out of AI — instead of confident guesses. Continue reading on Medium »

Subtitle: Understanding the fatal flaws of linear Retrieve-and-Generate pipelines — and why the industry is moving to Agentic workflows. Continue reading on Med

Everyone’s first retrieval-augmented system looks the same: chop the documents into pieces, turn each piece into a vector, search the… Continue reading on Mediu

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

Vector databases store meaning-numbers and find close matches — the retrieval shelf behind most RAG systems. Continue reading on Medium »

Why memorizing for the exam doesn't mean you understand the subject The post Water Cooler Small Talk, Ep. 11: Overfitting in RAG evaluation appeared first on To

Enterprise Document Intelligence [Vol.1 #M1] - The thesis behind every architectural choice in this series The post Amplify the Expert: A Philosophy for Buildin

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

Business users ask for reports. Data engineers write SQL. 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 »

What building a clinical RAG system taught me about retrieval, evaluation, and the gap between “it works” and “I trust it” Continue reading on Medium »

Enterprise Document Intelligence [Vol.1 #6c] - The decisions the parser makes on top of the user string, using the document’s profile: dispatch, activations, fu

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

Enterprise Document Intelligence [Vol.1 #5A] - Document signals (metadata, native TOC, source software) and page-level content (text vs scans, tables, images, c

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

Enterprise Document Intelligence [Vol.1 #4bis] - A coauthor note on the brick-by-brick pitfalls that justified the four-brick split, before Part II walks the fi

I want to learn some advanced RAG topics. So far I've learnt the basics of RAG, hybrid RAG, using RBAC and all. How do I go about learning more advanced topics

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 »

Discovering LangSmith Through a RAG Failure Continue reading on AI Quality Engineer »

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

Enterprise Document Intelligence [Vol.1 #3] - Why the ML toolkit (hyperparameter sweeps, train/test splits, explainability frameworks) solves the wrong problem,

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 »

The uncomfortable truth about AI evaluation, and the three-layer framework that separates engineers who ship from engineers who guess. Continue reading on Mediu

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 »

For AI engineers who want to understand every step, not just call the library The post Enterprise Document Intelligence: A Series on Building RAG Brick by Brick