An AI executive assistant is not a chatbot that runs your calendar. It is a persistent context layer plus a prompt habit that turns a general model into something that knows your role, your standards, and the shape of work you repeat every week. I use one daily for program prep, writing, and research. This is how I would set one up if I were starting from zero.
What you are building. Three pieces: a profile document the model can read, a short rules file for voice and boundaries, and a handful of reusable prompt templates for tasks you do often. You do not need code. You need fifteen minutes of setup and a willingness to iterate the first week.
Pick a surface. If you are non-technical, start with ChatGPT or Claude in a paid tier so you get longer context and project folders. If you are technical and live in an IDE, Cursor or similar gives you file access and is closer to how I work. The setup below works in any of them. What changes is where you store the profile doc, not the structure of the prompts.
Step 1: Write a one-page profile. Name your role, company context, what you own, who your stakeholders are, and what good output looks like for you. Include three recurring tasks you want help with this month. Keep it factual. The model will mirror vagueness back at you.
Step 2: Write five rules. Mine cover voice (direct, no filler), format (lead with the answer), privacy (never paste customer data or credentials), verification (show assumptions when numbers are involved), and continuity (refer to the profile instead of re-explaining my background every time). Your rules should match how you actually work, not how a blog post says you should.
Step 3: Create a project or folder. In ChatGPT or Claude, open a project and attach the profile and rules as instructions or pinned files. In Cursor, put them in a small folder and reference them at the start of sessions. The goal is one place you always start from so the assistant does not reset to generic every morning.
Step 4: Learn one prompt shape. I use six blocks in order: Role, Context, Task, Constraints, Output format, Quality bar. Role is who the model should be for this request. Context is the minimum background needed. Task is the single ask. Constraints are what to avoid. Output format is length, bullets, table, or draft email. Quality bar is how you will judge success. You can copy a filled example from the AI assistant prompt builder on this site.
Step 5: Save four starter prompts. Meeting prep: paste the agenda, ask for the three decisions needed, the two risks to surface, and the one thing to defer. Status scrub: paste a rough update, ask for a version an executive reads in ninety seconds with RAG health and decisions needed. Email draft: paste bullet notes, ask for a warm professional note with one clear ask. Decision memo: describe options, ask for a recommendation with trade-offs and what would change the answer. Reuse these verbatim for two weeks before you customize.
If you are new to AI, do this in week one. Day one: run the profile through the model and ask it to list what is still missing. Day two: use one starter prompt on real work, not a toy example. Day three: when the output is wrong, fix the prompt instead of accepting generic text. Day four: add one rule based on what went wrong. Day five: save the best prompt variant as your default. That is enough to feel the leverage without pretending you built an agent.
Boundaries for work use. Do not paste secrets, unreleased financials, customer PII, or material non-public information into a consumer AI account unless your employer allows it. Use enterprise accounts and data policies when they exist. An executive assistant is for drafting, structuring, and thinking. You stay accountable for what ships.
How this connects to program work. The same prompt discipline scales to team AI adoption. When I run enterprise AI programs, the teams that succeed teach people a small set of templates and guardrails instead of open-ended "just ask the model." Your personal setup is a low-risk place to learn that pattern before you roll it org-wide. Already using AI daily? Read Leveling Up When You Already Use AI for habits, measurement, and what to fix when output still feels generic. For the program side, see the AI program management hub and enterprise AI adoption.