Building AI Features That Earn Their Place
A practical framework for deciding when AI improves a product and when conventional software remains the better solution. Building reliable LLM features requires strict schema enforcement, sensible fallbacks, and treating model output as untrusted input.
“The goal is not to add a chatbot; it is to make a difficult operational task meaningfully easier.”
Most AI demonstrations break down the moment they encounter dirty production data, edge-case user inputs, or real latency constraints. Generative models are probabilistic by nature, while business software demands deterministic guarantees.
Building dependable AI features requires treating LLM outputs exactly like untrusted user inputs: enforcing strict JSON schemas, validating foreign keys against database records, and providing immediate fallback workflows when confidence scores drop.
Workflow Integration Over Conversational Novelty
Chatbots are often an anti-pattern. Frontline enterprise users do not want to type lengthy natural language prompts into an empty chat box; they want automated invoice categorization, intelligent diff detection, and instant document parsing embedded directly into their existing data entry forms.
Ground every AI capability in a concrete workflow. Enforce strict JSON schema validation, bound latency through streaming interfaces, and always provide a 1-click manual fallback when model confidence is low.
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