Everstone AI Blog

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 .