JTJAY TANKinjaytank.hashnode.dev·18h ago · 8 min readYour RAG isn't broken - your chunks areWhen a RAG system retrieves the wrong context, the debugging instinct is to reach for the expensive parts. Swap the embedding model. Tune the top_k. Add a reranker. Argue about cosine versus dot produ00
TBTanmay Bhurkundeinquery-to-scale.hashnode.dev·1d ago · 5 min readThe 10 Hidden Lifecycles Inside a RAG System (Beyond Chunk → Embed → Search → LLM)When I started building a Retrieval-Augmented Generation system, I thought I understood the shape of it: chunk the documents, embed them, search on a query, hand the result to an LLM. Four steps, done00
JSJay Simonsindesignly.hashnode.dev·1d ago · 11 min readOpen Knowledge Format vs RAG: Why Your Agent Should Read a WikiMost agent stacks reach for a vector database the moment the model needs a fact it was not trained on. That instinct is reasonable when the answer is buried in years of tickets and leftover wiki pages20
RRajshreeinrjshree.hashnode.dev·1d ago · 25 min readYour RAG Isn't Broken. Your Retrieval Pipeline Is.author: "RAJश्री" A practical guide to diagnosing and improving Retrieval-Augmented Generation systems Most RAG systems don't fail because the LLM can't answer the question. They fail because the LLM00
PKPiyush Kumar Soniinai-powered-seo-audit.hashnode.dev·4d ago · 9 min readBuilding an AI-Powered SEO Audit Workflow with Python and LLMsTechnical SEO audits can be extremely valuable, but they are also repetitive. A typical audit involves crawling a website, checking metadata, finding broken links, reviewing headings, spotting indexat00
MKMudassir Khaninmudassirworks.hashnode.dev·4d ago · 9 min readContext Engineering for RAG: How to Fill the Context Window With the Right InformationMost RAG systems that disappoint in production do not fail at retrieval. They fail at assembly. The retriever returns something plausible, the prompt gets stuffed with all of it, and the model answers21K
DKDamir Karimovinblog.damir-karimov.com·5d ago · 22 min readScaling RAG Systems: Production Architecture, Performance, and Cost OptimizationThe first three parts of this series covered why production RAG systems fail and how the quality of the data foundation directly affects everything that comes after it. We looked at document ingestion11K
PBPankaj Batrainpankajbatra.hashnode.dev·5d ago · 12 min readChat With Any YouTube Video — My ₹0 RAG App Is Now Live (Here's How the Frontend Works)Paste any YouTube link. Chat with the video. Get summaries, study notes, and quizzes — every answer grounded with clickable timestamps that jump the embedded player to the exact moment. It's live, it'10
Ppranuthanjalikininextlabsblog.hashnode.dev·5d ago · 8 min readBuilding an Omnichannel Healthcare AI System - RAG Architecture, Multilingual NLP, and Live Agent Handover DesignHealthcare support has a structural problem most industries don't face the same way: the cost of a wrong answer is much higher than a slow one. A retail chatbot that gives an imperfect product recomme00
JTJAY TANKinjaytank.hashnode.dev·5d ago · 8 min readYour RAG isn't broken - your chunks areWhen a RAG system retrieves the wrong context, the debugging instinct is to reach for the expensive parts. Swap the embedding model. Tune the top_k. Add a reranker. Argue about cosine versus dot produ00