Designing AI workflows that help the product instead of decorating it
How I think about embedding LLM features into real workflows with clear inputs, safe outputs, and handoff points that teams can support.
- Published
- 2026-06-20
- Read time
- 5 min
AI features become useful when they reduce friction in a workflow people already care about. The goal is not to add a chatbot everywhere. The goal is to remove repeated manual work, speed up decisions, or improve the quality of a task someone already performs.
Define the job before choosing the model
I usually start by asking what the feature should reliably produce: a summary, a classification, a draft, a structured extraction, or a next-step recommendation. That answer shapes the interface more than model branding does.
When the product team is clear on the job, prompt design, evaluation, and fallback behavior become much easier to reason about.
Make the boundaries visible to users
Users should know when they are looking at generated text, when a result may be incomplete, and what action comes next. Clear labels, editable drafts, and human review steps do more for trust than abstract claims about intelligence.
Operational simplicity wins
The best AI feature is often the one that the team can monitor, debug, and evolve without heroics. Logging, prompt versioning, and straightforward downstream actions matter just as much as the model response itself.