What Counts as an AI Application?
An AI application combines a model with ordinary software engineering: user experience, domain data, tools, permissions, storage, monitoring, and recovery. The model may generate text, classify input, extract structured fields, interpret images, or choose a tool. Your application remains responsible for authentication, authorization, data validation, business rules, and the final side effect.
A useful mental model is model + context + tools + control loop + product boundary. A prompt alone is rarely a complete product. Production systems need predictable contracts, clear failure behavior, latency and cost budgets, and ways to evaluate whether the output is useful.
The strongest AI products combine model flexibility with ordinary software controls. Never let a prompt be your only security boundary.
User interface
-> application API
-> policy and input validation
-> model request (instructions + context)
-> optional tool calls
-> output validation
-> persistence / response
-> telemetry and evaluation- Treat the model as a probabilistic component inside a deterministic application.
- Validate inputs and outputs at trust boundaries.
- Design failures, costs, and permissions before adding features.
Which responsibility should remain with application code rather than be delegated blindly to the model?