AI Workflow Ideas for Research, Writing, and Daily Planning

An AI workflow is a repeatable sequence with a defined input, a set of steps, and a check at the end before the output is used. The ideas here are concrete: each one names the input, describes the steps, and identifies where a person should review the result. Tools like fenbs let you save the prompts that work and record what each run teaches you, so the workflow improves rather than restarts each time.

A research workflow

Use this when you need to understand a topic quickly before writing, deciding, or presenting on it. The AI handles gathering and organizing; you handle deciding what is relevant.

• Input: the question you need answered and the sources you want the AI to work from (links, documents, or a specified scope).
• Step 1: ask the AI to summarize the three to five most relevant findings from the sources, in a specified format.
• Step 2: ask the AI to identify what the sources disagree on and what they leave unclear.
• Step 3: ask the AI to list what a reader would still need to verify from a primary source before acting on the summary.
• Check: review the summary against one of the original sources. If the AI cited something it cannot have read, ask it to limit itself to what was provided.
• Stopping point: when you can explain the three main findings in your own words, the research step is done.

This workflow produces a reliable starting point rather than a finished answer. The stopping-point check is the most important part: if you cannot explain the findings, the AI output has not yet done its job.

A writing workflow

Use this for structured documents: reports, proposals, client updates, or any piece with a defined format. The AI produces a draft; you review it against the standard the document must meet.

• Input: the outline or format, the audience, the key points to cover, and any style constraints.
• Step 1: ask the AI to draft the document section by section, not all at once. Review each section before asking for the next.
• Step 2: ask the AI to review its own draft for consistency: does each section follow from the one before it?
• Step 3: read the draft aloud or paste it into your review checklist. Mark anything that requires a decision you have not made yet.
• Check: any sentence that assumes a fact you have not confirmed needs either a source or a note that it is still to be verified.
• Stopping point: when all open items are either resolved or explicitly marked as outstanding.

The section-by-section approach takes more prompts but produces better output than asking for the full document at once. A long document generated in one pass often contains internal contradictions that are hard to catch until the document is in use.

A daily planning workflow

Use this at the start of a work day or session to turn a list of tasks into an ordered plan. The AI organizes and surfaces conflicts; you decide what to do.

• Input: your current task list, any deadlines or time constraints, and any dependencies between tasks.
• Step 1: ask the AI to group the tasks by type or project and flag any that depend on an earlier step.
• Step 2: ask the AI to suggest an order based on the deadlines and dependencies you provided.
• Step 3: review the suggestion. Move anything the AI could not know about: a meeting that changed, a decision that is pending, a task that is blocked.
• Check: the final plan should have no more tasks than you can realistically complete in the session.
• Stopping point: when you can commit to the first two tasks without needing to reconsider the rest.

A code review workflow

Use this when reviewing a pull request or checking a colleague's code before it merges. The AI checks for the patterns it can see; you check for the intent and context it cannot.

• Input: the diff or the files to review, and the criteria to check against (style guide, security rules, performance limits).
• Step 1: ask the AI to list potential issues organized by category: correctness, security, style, and performance.
• Step 2: ask the AI to flag anything it is uncertain about rather than guessing.
• Step 3: review the AI's list. Check each flagged item against the code and the intent behind the change.
• Check: any item marked uncertain by the AI needs a human to verify before the review is complete.
• Stopping point: when every item is either resolved, dismissed with a reason, or raised with the author.

For advertising workflows and similar review-focused processes, the stopping point and check are the most transferable parts of any of these ideas.

Saving prompts and lessons from each run

Each time you run one of these workflows, at least one prompt will work better than expected and at least one step will produce a surprise. Both are worth recording. The prompt that worked is worth saving as a reusable template. The surprise is worth recording as a lesson so it does not happen again.

A prompt library that grows from real use is more useful than one built from templates. After ten runs, the prompts you have actually tested are tailored to your inputs, your audience, and your quality standard. A template you downloaded is tailored to someone else's. The ai-workflow-library guide covers how to evaluate templates from external sources and adapt them to your situation.

How to do this with fenbs

Pick one workflow from above and set it up as a project in fenbs. Add the steps as tasks. Write the prompts in Skills and prompts as you test them. Record each lesson as it happens.

1. Create a free account. Add a project for the workflow you want to make repeatable.
2. Add the steps as tasks in the project. Write the input, the prompt, and the check for each step in the task plan.
3. Run the workflow once and note what worked and what did not. Add the prompts that worked to Skills and prompts.
4. Record any surprise as a lesson: what happened, what you learned, and what to do differently next time.
5. Connect your AI app from Integrations. Ask it to read the project skills and lessons before starting the next run.