HSHaithem Slimiinnightthoughts.me·1d ago · 9 min readRAG Explained for Beginners: How to Teach an AI to Use Its Own NotesYou've probably seen a chatbot confidently answer a question with the wrong company policy, a made-up feature, or a date that hadn't happened yet. That gap — between what a model knows and what you ac00
ABAbhishek Banerjeeindeeper-in-tech.hashnode.dev·Sep 19 · 7 min readThe Invisible Cost of Context Windows: Why Vector Databases Are Reaching Their LimitsAs LLMs cross the million-token threshold, the trade-offs of vector search are shifting. Here is why high-dimensional indexes fail at scale and where enterprise retrieval is actually heading. Naviga00
TBTanmay Bhurkundeinquery-to-scale.hashnode.dev·Aug 6 · 4 min readRAGnarok Part 1: Why I'm Building an Enterprise Knowledge Assistant (Not Another "Chat With Your PDF")Building an Enterprise RAG System in Public — A Data Engineer's Log Starting a series: RAGnarok I'm kicking off RAGnarok — a build-in-public series where I go from RAG fundamentals to a genuinely pro00
MNMarc Newsteadinicentric-ai-and-automation.hashnode.dev·Jul 27 · 4 min readWhy Your RAG System Fails at Multi-Hop Questions (And How to Fix It)The Problem Nobody Talks About You've built a RAG system. It works beautifully for simple lookups. Ask it "What's our refund policy?" and it nails it every time. But ask it something like "Which custo00
ABAditya Biranjeinadityabiranje210.hashnode.dev·Jul 23 · 27 min readDesigning the Ingestion Layer of a Production RAG SystemMost RAG postmortems point at retrieval. Wrong chunks come back, the reranker doesn't help, the LLM hallucinates around a gap in context. Almost none of it is a retrieval bug. By the time a query hits13A
MMaverickinjyanshu.hashnode.dev·Jul 13 · 13 min readVector EmbeddingsHow AI Learned to Understand the Meaning Behind Words Who is this for? You don't need to know any math, code, or machine learning. If you've ever used Google Search, gotten a Netflix recommendation,00
TStarini sunilinai-content-utilties.hashnode.dev·Jun 22 · 4 min readWhat Your Documents Mean, Not Just What They SayOn this page A Tiny Experiment Why Keywords Fail Meaning Without Matching Words The Hidden Layer A Simple Mental Model Try It Yourself The Aha Moment The Hidden Signal In the previous artic00
MSManuela Schrittwieserinneuralstackms.tech·Jun 8 · 12 min readThe Hidden Attack Surface of Vector DatabasesNeuralStack | MS Tech Blog – Databases & Data Engineering in AI Security Engineering, Part 2 of 4 A New Storage Primitive, A New Threat Model Vector databases emerged as a distinct infrastructure cat00
TStarini sunilinai-content-utilties.hashnode.dev·May 30 · 5 min readWhat Your Documents Whisper When Nobody's LookingMost people think important information is easy to spot. Look at enough documents and you will find the important topics because they appear again and again. More mentions = more importance. Right? No00
KJKawal Jainininsight.vectastack.com·May 1 · 11 min readWhy Most RAG Pipelines Fail in Production You’ve seen the tutorial. It’s 15 lines of Python using a popular orchestration framework. You load three clean markdown files, initialize an in-memory vector store, pass a query to an LLM, and watch 00
