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https://rubystacknews.com/2026/06/01/the-original-sin-the-scorpion-and-local-ai/
Air Canada found out the hard way. Here’s why naive RAG hallucinates even when the right document is sitting right there.Continue reading on AI Engineering Simplified »
A complete technical breakdown of every failure point in basic RAG pipelines — and the strategies modern systems use to fix them. Quick…Continue reading on Medium »
I recently attended a seminar presented by the venerable Andrew Bruce Smith. In it, he used that phrase, ‘retrieval augmented generation (RAG)’. I immediately asked myself why a te...
Raggie is a medium sized crossbreed aged around ten years old. Raggie fostered Norwich. Raggie was rescued from a kill shelter in Romania when he was a youngster and has lived in a...
The Benchmark Trap The retrieval-augmented generation (RAG) ecosystem has matured remarkably fast. Vector databases are production-grade, embedding models are cheaper than ever, an...
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 fixes The post 10 Comm...
Increasing context size in RAG systems doesn’t improve accuracy for aggregation tasks—it makes errors harder to detect. In this article, I benchmark retrieval-based pipelines again...
https://rubystacknews.com/2026/06/04/turning-a-generic-llm-into-a-ruby-expert-what-rag-fixed-and-what-it-didnt/
"Chat with your documents" sounds simple. Then you build it, and you discover a good RAG system is really eight systems wearing a trench coat. I recently finished myRAG — a full...
Ask your vector RAG pipeline "what are the main themes in this corpus?" and watch it return three random chunks that share a keyword. Flat vector retrieval is built for "find me th...
Retrieval-augmented generation (RAG) is now the default pattern for grounding large language models in private or domain-specific knowledge. Yet most RAG systems still hallucinate,...
Usually after it already answered something business-critical incorrectly.Continue reading on Medium »
The model is almost never the problem. Your retriever is.Continue reading on Cubed »
Enterprise Document Intelligence [Vol.1 #6a] - Why a user question deserves the same parsing as the document, and how it splits into a retrieval brief and a generation brief before...
Enterprise Document Intelligence [Vol.1 #M1] - The thesis behind every architectural choice in this series The post Amplify the Expert: A Philosophy for Building Enterprise RAG app...
The Difference Between Code and Documents Split a Python file into 1000-character chunks with RecursiveCharacterTextSplitter, embed them, run vector search — this is the most c...
Retrieval-Augmented Generation (RAG) has reshaped how modern AI systems are designed by allowing language models to access external knowledge at runtime. Instead of relying solely...
Enterprise Document Intelligence [Vol.1 #6ter] - Six positions on the question-parsing brick that contradict the mainstream RAG playbook The post The Untaught Lessons of RAG Questi...
“For People Who Aren’t AI Experts Yet”Continue reading on Medium »
Enterprise Document Intelligence [Vol. 1 #1] The smallest version of RAG that actually works, on a real PDF, with grounded answers and the source lines highlighted. The post Baseli...
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 typed LLM call The post...
The AI that checks its sources before answeringContinue reading on Medium »
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