AI Workflow Prompts: Define Inputs, Checks, and Handoffs

An AI workflow prompt does more than describe a task — it defines the inputs the AI should use, the checks it should perform before proceeding, and the point at which it should stop and wait for a person to decide. Without those boundaries, an AI assistant will complete what it thinks the task requires, which is often more or less than what you actually intended. Tools like fenbs let you keep that prompt library and the project instructions it relies on in one place, so every assistant you connect starts a session from the same agreed set of rules.

The prompts below are designed for three stages of workflow execution: starting a workflow with a clear scope, carrying out individual steps with explicit evidence requirements, and handing off to the next person or session with a complete record of what happened.

Why workflow prompts need stopping points

An AI assistant given a multi-step workflow will keep going until it runs out of steps or hits a technical limit. That continuity is useful for low-stakes automation — summarizing a list of articles, formatting a spreadsheet — and risky for anything where a wrong decision midway changes the outcome significantly.

Stopping points are explicit moments in the prompt where the assistant must pause and wait for a human decision before continuing. They turn a continuous run into a series of approved steps. The cost is a few confirmation messages. The benefit is that no part of the workflow proceeds without someone checking that the previous part produced the right output.

For a useful introduction to approval steps in AI workflows and how to structure them, see AI agent approval workflow. For the broader picture of what AI agents can do at work and where people should stay in charge, see AI employees.

Prompts for starting a workflow with a clear scope

The starting prompt sets the boundaries for the whole workflow. It names the inputs, the expected output, the rules the assistant must follow, and the points where it should stop. Writing it carefully at the start saves correction time later.

I want you to carry out this workflow: [describe the workflow in 2–4 sentences]

Inputs you have: [list each input and where it comes from]
Expected output: [describe the deliverable]

Rules for this workflow:
1. Use only the inputs listed above. Do not search for additional information.
2. If an input is missing or unclear, ask me before proceeding — do not assume.
3. After completing each step, summarize what you did and what you used. Wait for my confirmation before moving to the next step.
4. If you reach a decision that requires judgment you do not have the context for, stop and ask.

Starting step: [describe only the first step]

Notice that the starting prompt gives only the first step. The full workflow is described at the top so the assistant understands the context, but it is given one step at a time. This pattern prevents the model from skipping ahead when it thinks it knows what is coming.

Prompts for carrying out a step with evidence

Each workflow step should end with an evidence summary: what the assistant used, what it produced, and what it checked. An evidence summary gives you something to review before approving the next step. Without it, you are approving something you cannot verify.

Complete this step: [describe the step]

When you finish, produce an evidence summary in this format:
— What I used: [list the inputs or documents you drew from]
— What I produced: [describe the output]
— What I checked: [list any verification steps you performed]
— What remains uncertain: [anything you could not confirm]

Do not start the next step until I confirm this summary is correct.

Before I review this output, check it against these criteria:

1. Does it use only the inputs I provided? If not, flag what came from elsewhere.
2. Does it answer the question I asked, not a similar question? Confirm.
3. Is there anything in the output that requires my judgment to verify? List it.

After checking, give me a short "ready to review" or "needs correction" verdict with a reason.

These prompts ask the model to catch its own errors before you have to. The model will not catch everything, but an explicit self-check before handoff catches the issues a model can identify — missing inputs, off-topic conclusions, sections that depend on assumptions you did not provide.

Prompts for handling uncertain or missing inputs

A common failure mode in AI workflows is the model proceeding when it should stop. When an input is missing or a decision is ambiguous, it fills the gap with its best guess rather than surfacing the uncertainty. These prompts build in explicit guards against that.

Before you start this workflow, review the inputs I gave you and list:

1. Any input that is missing and would change the output if it were provided.
2. Any instruction that is ambiguous and could reasonably be interpreted in two or more ways.
3. Any decision I need to make before you can proceed.

Present each item as a question I need to answer. Do not start the first step until I have answered all of them.

Prompts for handoff and progress records

A workflow that stops partway through — because a session ended, a person needs to review, or the work carries over to the next day — needs a handoff record. The handoff tells the next session what was completed, what remains, and what is waiting on a decision.

This workflow session is ending. Create a handoff record in this format:

Workflow: [name]
Status: [in progress / awaiting review / blocked]

Completed steps:
— [step name]: [what was done, what was produced]

Remaining steps:
— [step name]: [what still needs to happen]

Waiting on:
— [decision or input needed from a person, with context]

Next session should start at: [step name and brief description]

Save this handoff record in your project notes at the end of each workflow session. The next assistant — even if it is a different model — reads the current state of the workflow and continues from the right step rather than restarting or guessing where to pick up. This is what the AI agent approval workflow pattern calls a clean handoff.

How to do this with fenbs

1. Create a free account and add a project for the workflow you are running.
2. Save each workflow prompt as a Skill with the step name, what inputs it needs, and what evidence format it should produce.
3. Add the workflow rules — what the assistant may and may not do, which steps require human approval — as Decisions and rules in the project.
4. At the end of each workflow session, run the handoff prompt and paste the result as a context note in fenbs. The next session reads this note before starting.
5. Connect your AI app over MCP from Integrations. Ask the assistant to read the project context and confirm the current workflow state before taking any step.

fenbs does not run workflows or automate steps on your behalf. It keeps the prompt library and the rules that make each workflow run the same way every time, and the handoff records that let a new session continue without restarting. For project-level planning prompts that complement workflow execution, see prompts for project management AI.