Adding AI to a web product without the hype

Almost every product can bolt a chatbot on. Far fewer benefit from it. The difference is picking a real user problem first and a model second.

Find the repetitive, language-shaped work

Language models are strongest where the task is drafting, summarising, classifying or searching text. If your users already spend time writing similar messages, reading long documents or sorting incoming requests, that is a promising place to start.

Keep the human in control

Treat model output as a suggestion. Let people review, edit and approve before anything is sent or saved. This builds trust, and it protects you from the occasional wrong answer.

Design for the slow, failing case

Model calls can be slow, expensive and occasionally unavailable. Plan for it:

• Show clear loading states, and stream responses where it helps
• Set timeouts and retry sensibly
• Provide a graceful fallback when the call fails
• Validate and limit inputs before they reach the model

Protect keys and data

API keys belong on the server, never in the browser. Decide up front what user data may be sent to a provider, and tell users plainly what happens with it.

Start small and measure

Ship one focused feature, watch how people use it, and improve the prompts and the interface from real behaviour. A modest feature that works reliably is worth far more than an ambitious one that does not.