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"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...
https://rubystacknews.com/2026/06/01/the-original-sin-the-scorpion-and-local-ai/
A technical walkthrough of a production-patterned RAG pipeline built for security analytics — the design decisions, the evaluation…Continue reading on Medium В»
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...
Learning how to build rag starts with a simple pipeline: prepare trusted source data, split it into useful chunks, create embeddings, store those embeddings in a searchable index,...
How I designed a three-layer conversational memory system that actually performs and why the obvious solution wasn’t the right one.Continue reading on Medium В»
Retrieval-augmented generation (RAG) has become the de facto standard for grounding large language models (LLMs) in private data. The standard architecture — chunking documents, em...
Vector databases are a temporary bridge. Discover why the next AI infrastructure revolution relies on persistent neural state and strict latency budgets, not on vector databases. T...
The model is almost never the problem. Your retriever is.Continue reading on Cubed »
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...
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 »
https://rubystacknews.com/2026/06/04/turning-a-generic-llm-into-a-ruby-expert-what-rag-fixed-and-what-it-didnt/
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 »
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...
Over the years, I have spent a fair amount of time building systems that need to work reliably in production.Continue reading on Medium »
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...
Retrieval Augmented Generation (RAG) is an AI architecture pattern that connects...
I'm building a benchmarking platform to rigorously compare three AI retrieval pipelines on a large corpus of Indian public health research papers from PubMed Central. Here's the ar...
Retrieval-augmented generation (RAG) caught on fast — and for good reason. Connecting a large language model to your organization's documents feels like the most natural way to bui...
A technical comparison of Proxy-Pointer and LLM-Wiki The post Proxy-Pointer RAG: Temporal Reasoning Without Semantic Precompilation appeared first on Towards Data Science.
Enterprise Document Intelligence [Vol.1 #6c] - The decisions the parser makes on top of the user string, using the document’s profile: dispatch, activations, full schema, three app...
Traditional RAG fails on multi-step questions. Agentic RAG adds planning, self-correction, and tool use. Here’s the architecture, real…Continue reading on Medium »
Enterprise Document Intelligence [Vol.1 #9A] - Same paper, same question as Article 1. One upgraded contract per brick: document parsing, question parsing, retrieval, generation Th...
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...
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