Building practical AI tools
10 January 2026 | 4 min read
Notes on moving from impressive demos to AI systems that people can rely on in real workflows.
The easiest AI tools to demo are often the hardest to trust in production. A polished interface can hide brittle prompting, unclear failure modes, and no real operational ownership.
Practical AI tools start from a narrower question: what specific task is slow, repetitive, or error-prone today, and what part of that workflow benefits from a model rather than conventional software?
That framing changes the implementation. Instead of building around novelty, you build around reliability: clear inputs, explicit outputs, good defaults, and enough logging to understand when the system helped or failed.
The bar is not whether the model can do something impressive once. The bar is whether a teammate can use it repeatedly without losing context, confidence, or control.