Beginner's guide to making AI useful in production (medium.com)

Most AI demos fail in production because models either hallucinate, return stale retrieved content, or botch tool calls by inventing table or function names. The author presents a pragmatic architecture that fixes this: combine “learned concepts” (structured recipes like SOPs, decision trees and explicit query patterns) with runtime tools (live SQL, internal APIs, real-time fetches exposed via protocols like MCP) and a lightweight orchestrator. The orchestrator classifies a user question, attaches the relevant concept, exposes only the needed tools, lets the model execute queries, and validates results—turning the model into a junior analyst that knows how to fetch and verify data rather than invent answers. Technically this is less about new models and more about composition: use concise, machine-readable concepts instead of dumping documents into vectors; provide explicit tool interfaces so the LLM can execute queries safely; and centralize control in an orchestrator to avoid expensive context bloat, prevent hallucinated function calls, and enable fast iteration. Benefits seen in production include higher reliability, lower costs, faster onboarding of new data sources (no retraining), and increased stakeholder trust. This is Part 1 of a three-part series; upcoming posts will detail system design, data flows, and production reliability strategies.