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...

 There’s a common pattern in enterprise AI: users ask the same questions again and again. Think about your internal knowledge base. Employees constantly ask things like: “How do I reset my VPN?” “What are the Q3 sales figures?” “What’s the company policy on expense reports?” A standard RAG system has no memory of these past interactions. Every time a question comes in, it repeats the same expensive steps: Embed the query Search the vector database Retrieve the top documents Send everything to the LLM (GPT-4, Llama, etc.) Generate an answer Even if the question—and the answer—was identical five minutes ago. This is incredibly wasteful. You’re paying for retrieval and inference on every request, even when nothing has changed. It’s like running a factory production line just to print the same invoice twice. The result is predictable: rising costs and unnecessary latency. The fix is semantic caching —a technique that recognizes repeat intent and serves answers instantly, without re-run...

There’s a dirty secret in the GenAI world: building a demo is easy. Building a product is hard . In an afternoon, you can ship a Proof of Concept chatbot that answers correctly 80% of the time. But in an enterprise setting—especially Finance, Healthcare, or Legal—that remaining 20% isn’t just an annoyance. It’s liability. If a support bot hallucinates a refund policy, you lose money. If a legal bot cites a clause that doesn’t exist, you get sued. The root cause is usually the same: most PoCs rely on pure Vector Search (semantic similarity) . It’s great at concepts, but it’s weak at precision. It can confuse “Product A” with “Product B” simply because the wording is similar. To move from a fragile demo to a high-fidelity RAG system , you can’t rely on the “magic” of the LLM. You need to engineer reliability into retrieval, ranking, prompting, and governance . Here’s a practical blueprint using Databricks Mosaic AI tools. 1) The Retrieval Fix: Hybrid Search Standard vector search convert...

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 .

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...