AI application development

Design AI features as part of a real application—not as a demo.

An AI feature becomes useful software only when the surrounding application knows what the model can do, what it cannot guarantee, how outputs are validated, and what happens when the model is wrong or unavailable.

By Sanjai Syamaprasad · Software Engineer & AI Application Developer · September 29, 2026

1. Define the model boundary

I first identify the narrow task the model is responsible for: extraction, classification, summarization, drafting, or another bounded transformation. The rest of the application should not become vague simply because one component is probabilistic.

2. Structure the output

Free-form text is easy to demo and harder to integrate. When the application needs fields, statuses, or actions, I prefer structured outputs that can be checked against a schema before they affect product state.

3. Validate before persistence

Model output should be treated as untrusted input. Required fields, allowed values, ownership rules, and database constraints still belong to deterministic application code.

4. Preserve human review where judgment matters

If the result influences an important decision, the interface should make the source information and AI-assisted result easy to inspect. The model can reduce friction without becoming an invisible decision-maker.

5. Build fallbacks and measure usefulness

A useful feature should have a path when the model times out, returns weak output, or becomes temporarily unavailable. I evaluate the feature by whether it improves the workflow—not by how prominent the AI label is.

What I optimize for

I optimize for software that can be explained, tested, observed, and changed without forcing the next engineer—or my future self—to rediscover the system from scratch. That usually means making state explicit, keeping boundaries clear, and choosing the simplest architecture that can support the real workflow.