This article assumes you already know what RAG is, why naive RAG breaks at scale, and what chunking, embedding, and retrieval mean. We skip the basics.The Problem with Text-Only RAG at ScaleStandard RAG pipelines assume your knowledge base is text. Tha…
AbstractContinue reading on Medium »
L’une cherche l’information au bon moment, l’autre la prépare à l’avance. Comprendre ces deux concepts avec des exemples simples, concrets…Continue reading on Medium »
A production-ready enterprise RAG+MCPMost RAG demos stop at the fun part: an embedding model wired to a vector store, tweak every knob possible (hybrid retrieval, contextual retrieval, graph-based pre-chunking document tuning, etc, etc), and once it pe…
How Agentic RAG, semantic layers, vector search, lakehouse architecture, and data governance are changing the way companies build modern…Continue reading on DataDrivenInvestor »
When a job posting surfaces asking for a “prompt engineer” with expertise in distributed systems, API design, machine learning operations, security engineering, and product management all at once, the instinct is to laugh. But the uncomfortable truth i…
Your RAG Agent Forgets Everything After One Message – Here’s How I Fixed It with Databricks LakebaseBuilding a context-aware RAG system end-to-end: from PDF parsing to multi-turn conversations that actually rememberMost RAG tutorials show you how to bu…
The Silicon Protocol: How to Optimize LLM Context Windows Without Breaking Production Systems in Healthcare, Finance & Government (2026 Guide)200K tokens cost $47 per request. Your model stopped paying attention at 80K. The bill arrives anyway.Thre…
Agentic RAG is not just “more LLM calls” on top of traditional RAG. It is a shift from static lookup to dynamic control: deciding what to…Continue reading on Towards AI »
Günümüzde biz yazılımcılar arasında ve özellikle yapay zekaya ilgi duyan, geliştirme yapan yazılım sektörü emektarları veya yapay zekaya…Continue reading on Medium »