Everstone AI Blog

 In traditional data warehousing, dirty data usually means null values or duplicates. We fix that with simple, deterministic rules. In Generative AI, dirty data is far more dangerous. It shows up as semantic noise —and it can change what the model believes is true. Consider a financial-services chatbot powered by RAG (Retrieval-Augmented Generation). It ingests thousands of marketing PDFs. Every page contains a legal footer: “This document contains forward-looking statements that are not guarantees of future performance.” Now a user asks, “What is the projected growth?” The retriever pulls the footer. The model reads the legal language, misinterprets it, and responds: “The company guarantees future performance.” That isn’t a UX bug. It’s a liability. This is the new reality of data hygiene. You can’t just dump raw PDFs into a vector database and hope for the best. You must clean them first. And because the problem is semantic, not structural, standard code isn’t enough. You need an...

In AI engineering, there’s a pattern I call “Magpie Architecture.” An engineer spots a shiny technique—HyDE, knowledge graphs, cross-encoder reranking—and the next instinct is to add it straight into production. The argument is always the same: “It will make the answers better.” Sometimes it will. But in a business context, “better” has a price tag. Every added layer in a Retrieval-Augmented Generation (RAG) stack typically increases:     • Latency (how long users wait),     • Compute cost (your infrastructure bill), and     • Operational complexity (more parts to own, test, and maintain). So as a manager or architect, the real question becomes: Is the marginal gain in quality worth the marginal increase in cost? Here’s a practical way to stop guessing and start measuring ROI using Databricks Vector Search and MLflow evaluation. The “Good Enough” Baseline Before you optimize, you need a baseline. For most RAG systems, that baseline is Approximate Nearest...