A useful AI assistant needs to understand the job it is being asked to do. It needs enough context to ask a relevant question, produce a usable draft, and leave the person using it able to decide what happens next. Much of my work has been about making that setup easier to repeat.
Turning an idea into a project
At Nerdy, I created project-building and prompt-engineering resources that helped colleagues describe a task and assemble the instructions and context for an assistant. I taught the approach so people could adapt it to their own work.
Organizing the working day
I also developed and taught a reusable work-organization assistant. It helped bring information into a form a person could review, prioritize, and act on. A colleague's reuse of the approach was one useful sign that the pattern could travel beyond my own setup.
The emphasis was on giving the person a clearer working view. They still made the decisions, checked what was missing, and chose which actions to take.
Helping a request reach engineering
Another workflow guided colleagues through describing a request for an engineering ticket. It brought the problem and relevant context into the conversation so the request could be made more specific before reaching the team.
That experience informs how I approach product development: understand the person and the task, make the context available, and decide where the system should ask for a human decision. I bring the same approach to custom applications, retrieval, plugins, and MCP integrations in my independent work.