How to Design a Reusable AI Assistant

How to Design a Reusable AI Assistant

Design a reusable AI assistant by giving one named user dependable help with one recurring task. Define the inputs, evidence, output, non-goals, permissions, review, failure behavior, and owner before choosing a persona or product.

The first deliverable is not a prompt. It is a one-page assistant contract.

flowchart TD
    A["One user and recurring task"] --> B["Accepted inputs and approved sources"]
    B --> C["Required output and success"]
    C --> D["Non-goals and least authority"]
    D --> E["Uncertainty and escalation"]
    E --> F["Representative test set"]
    F --> G["Human-reviewed pilot"]
    G --> H["Revise, monitor, or retire"]

Choose work that is already understood

Start with a task the owner can judge. Note organization, meeting-summary drafting, content-outline preparation, and conversion of an approved export into a draft report can be good candidates.

Avoid beginning with a task whose correct answer is unclear or whose failure can immediately affect health, money, legal rights, employment, security, or the public. An assistant cannot compensate for missing process ownership.

E033's Venture Episode Engine worked as a useful experiment because Dalton already had a podcast-outline method. He knew the source material, the preferred section order, and the kind of result he wanted. The assistant transformed a familiar task.

Write the job as an observable transformation

"Help with podcasts" is too broad. "Turn my approved notes and spoken ideas into a draft outline that follows the current Venture Step structure" can be tested.

Name the intended user and the point at which the result stops. If the assistant drafts an outline, say that it does not research missing facts, approve claims, record the episode, or publish the page.

A tight job keeps the configuration small and makes failure visible.

Define success before instructions

Success should reflect the whole workflow, not just the first output.

For an outline assistant, success might mean that every approved idea is represented, the section order is correct, unsupported claims are marked, and the owner can finish review in a defined amount of time. A polished draft that requires a complete factual audit may not be successful.

Keep a baseline. Compare the assistant-assisted task with the prior method using the same kind of input. Record time, corrections, missed requirements, and reviewer effort.

NIST's AI Risk Management Framework organizes risk work around govern, map, measure, and manage. That sequence is useful here: own the decision, understand the task, measure performance, then control the deployment.

Control the inputs and evidence

Describe the inputs the assistant may receive. Include their format, owner, sensitivity, and expected quality.

Then define the evidence hierarchy. Some tasks use only the supplied notes. Others need current official documentation. Some require a canonical company record or a qualified reviewer.

If the system cannot browse, do not tell it to use "the latest information." If it can browse, define the allowed sources and how it should show what it used. If a knowledge file is attached, record its version and decide whether later changes should flow into the assistant automatically.

Google's current Gem documentation supports optional knowledge files, including certain Drive sources. A live connection can improve freshness while also changing the tested source set. Treat that as a configuration decision.

Describe the output a reviewer needs

The output should make review easier.

For a content outline, that may mean a clear title, audience, angle, source boundary, section spine, open questions, and excluded claims. For a report draft, it may mean a period, source file, calculations, exceptions, and approval status.

Do not over-specify decorative formatting. E033 captured a small but telling problem: the Episode Engine bolded more text than Dalton wanted. The output looked structured, but the formatting made it harder to scan.

Specify format only when it helps the next user or system.

Write the non-goals

Non-goals are part of the product.

State which topics, actions, data, audiences, and decisions are outside scope. A note organizer should not provide financial advice because a note mentions a budget. An outline assistant should not invent guest facts because the transcript is incomplete. A travel draft should not present a generated itinerary as verified operating information.

The live E033 travel planner omitted Dalton's work schedule because the input omitted it. A good contract would either require availability before planning or label the itinerary incomplete.

Grant the least authority

A drafting assistant usually needs no external write access. Keep it that way until testing shows a real reason to add more.

When tools are required, list the exact read and write operations, account, destination, approval point, logging, retry behavior, and rollback. "Access to email" is not a permission model.

If a product allows sharing, inspect the exposure. Google's current Gem sharing help says users with access can view instructions and uploaded files. Sharing a configuration can therefore share its evidence.

Make uncertainty and escalation useful

Telling the assistant to "be honest" is not enough. Define observable behavior.

When a required input is missing, it should name the missing field and stop that part of the task. When approved sources conflict, it should show the conflict rather than choose silently. When a request exceeds permission, it should prepare a draft or handoff rather than take the action.

Name the reviewer. "Ask a human" is vague. "Return the draft to the episode owner for source approval" creates a path.

Build the first test set

Use at least one ordinary case, one messy case, one missing-input case, one conflicting-instruction case, and one out-of-scope case. Add privacy and permission cases when the assistant can receive sensitive information or use tools.

The expected result does not always need exact wording. It can describe required properties, forbidden behavior, and maximum acceptable reviewer effort.

Run the same set after instruction, source, model, tool, audience, or product changes.

Pilot with human review

Keep every output in draft state during the pilot. Preserve the input, configuration version, output, corrections, and reviewer decision.

Look for repeated failure patterns. If the model misses the same source rule, improve the instructions or architecture. If the task remains hard to judge, narrow it. If review costs more than the old method, stop.

Current product documentation describes intended behavior, not your result. A vendor feature is not a completed evaluation.

Maintain or retire it

Assign an owner and review date. Record the product surface, instruction version, sources, known failures, test set, and last decision.

Retire the assistant when the task changes, the source set loses authority, the owner leaves, a product change breaks the boundary, or the benefit no longer justifies review.

The one-page contract should make that decision possible:

After the contract is clear, continue with [[How to Write Instructions for a Custom AI Assistant]]. Before depending on the result, use [[How to Test an AI Assistant on a Bounded Task]]. If the design may face strangers, stop at [[Private Productivity Assistant or Public AI Persona]] first.

This method guide was developed with AI assistance from E033, current Gemini and Meta records, the linked assistant contract, and NIST guidance. Dalton Anderson remains the author. Product, privacy, security, safety, technical, current-source, and founder review are mandatory before publication. Publication is not authorized.