Start Here: Build One AI Workflow in 20 Minutes

I’m going to be honest with you… you most likely do not need an AI operating system.

You probably do not need seven agents, a knowledge graph, or a dashboard showing which robot is currently summarizing the dashboard.

You need one piece of work that goes better than it did last time.

That is the smallest useful unit of AI adoption… not a tool account, prompt collection, or screenshot of a surprisingly competent answer. A workflow that takes a real input, produces something inspectable, passes a check, and helps you do actual work.

Twenty minutes is not enough to automate a company.

It is enough to stop treating AI like a slot machine.

This page is my endeavor to create the front door to the practical side of Don’t Feed The Algorithm.

Pick the problem that sounds most like yours, follow the timed setup, and build one workflow before you begin collecting more advice about workflows.

Do not choose based on which model currently has the most emotionally intense leaderboard discourse.

Choose based on the job.

Choose this if you have a real outcome and a pile of source material:

- customer feedback that needs to become a roadmap recommendation;

- research that needs to become a decision;

- campaign inputs that need to become a brief;

- founder chaos that needs to become a 30-day plan;

- account evidence that needs to become careful outreach;

- weekly notes that need to become an operating review.

Start with 13 Fable 5 Prompts I’d Actually Use.

That paid working file gives you thirteen copy-ready jobs across marketing, product, engineering, sales, customer success, operations, recruiting, research, writing, and knowledge systems.

Choose this path when: the work matters now, has several inputs, and should end in one usable artifact.

Choose this if the output is code, a product feature, a design change, or a tested build.

Start with Steal My 5 Best Claude Code Workflows.

That paid manual separates five jobs people usually collapse into “build this”:

1. interview the idea;

2. write the implementation plan;

3. build with separate specification and quality reviews;

4. fan out exploration, then converge;

5. verify the real user path and preserve what the repository learned.

Choose this path when: vague direction could create expensive code, and “the test passed” is not enough proof that the product works.

Choose this if you have already done the job once and expect to do it again:

- weekly customer-signal synthesis;

- recurring content opportunity research;

- a Friday operator review;

- launch readiness;

- campaign analysis;

- market monitoring;

- any recurring process where explaining the context again is becoming its own part-time job.

Start with The 5-File Claude Fable System I’d Use to Stop Starting From Zero.

That paid working file gives the job five durable parts:

JOB

SOURCES

GATES

STATE

REVIEW

Choose this path when: the first run was useful, but the next run needs memory, boundaries, verification, and a reason to exist again.

If you are still deciding, use this:

| Your situation | Start with | Your first artifact |

| “I need help completing a complex knowledge-work job.” | Path 1 | Brief, recommendation, plan, research synthesis, or operating review |

| “I need AI to build or change software safely.” | Path 2 | Approved design or implementation plan before code |

| “This useful job repeats, but every run forgets the last one.” | Path 3 | Five-file workflow contract and first saved state |

If two paths fit, begin with the earlier failure.

A recurring workflow built around a badly defined job will preserve the wrong thing with impressive consistency. A coding workflow without an approved product decision will implement ambiguity faster. Define the job first.

Open a blank note. Set a timer. Do not spend eleven minutes deciding what to call the folder.

Finish this sentence:

I want AI to help me ________________________________.

Use a verb and an artifact.

Weak:

Help with marketing.

Useful:

Turn the last ten customer calls into a source-backed launch brief.

Or:

Turn this approved feature idea into an implementation plan another agent can execute.

Why is this job happening now?

TRIGGER

This workflow starts when:

[an event occurs / enough new evidence exists / I make a manual request]

A trigger prevents “use AI more” from becoming a recurring item on your goals list without ever touching work.

List the material the workflow is allowed to use:

SOURCES

1.

2.

3.

Prefer primary material: customer calls, product documentation, current code, analytics, approved strategy, direct research, prior decisions, and real examples.

If the model has to guess where truth lives, the polished answer may simply hide the guess better.

Write three lines:

AI MAY:

AI MUST ASK BEFORE:

AI MUST NEVER:

Publication, external sends, product strategy, spending, deleting, customer promises, sensitive information, and destructive code changes should not quietly become model decisions because the output arrived in a reassuring tone.

What must exist when the workflow is done?

OUTPUT

The run must create:

FORMAT

It should live as:

OWNER

The human who uses or approves it is:

A summary is not automatically an artifact. A decision brief, implementation plan, tested change, campaign brief, customer-signal table, or weekly operating review can be inspected and used.

Complete this:

VERIFICATION

I will know this worked when:

Useful checks include:

- every important claim links to a source;

- the build and tests pass;

- the real user path works;

- required fields are complete;

- the output changes a decision or next action;

- a human reviewer can accept, reject, or revise it;

- the workflow saves enough state to avoid repeating work.

The model saying “I successfully completed the task” does not count. That is a status update from the person being graded.

Open the path you chose:

- Path 1: choose one of the 13 ready-to-run jobs

- Path 2: begin with the discovery interview before touching code

- Path 3: create the five files and run the setup prompt

Paste in the job, trigger, sources, boundary, output, and verification standard you just wrote.

You are now more prepared than most AI pilot committees, which is a low bar but still a bar.

Do not reply with “this was interesting.” I am trying to learn whether this helped you build something.

Hit reply and paste this:

PATH: 1 / 2 / 3

JOB:

TRIGGER:

FIRST ARTIFACT:

VERIFICATION:

HUMAN DECISION I KEPT:

DID I COMPLETE THE FIRST RUN? yes / no / not yet

WHAT GOT STUCK:

I will treat the workflow as activated only when the first run produces an inspectable artifact. Not when you click the link, save the post, or experience a temporary surge of organizational optimism.

That distinction matters because opening a tool is not adoption. Producing something useful is.

Set a reminder for seven days from now.

Reply to the same email with:

DID I USE THE ARTIFACT IN REAL WORK? yes / no

WHAT CHANGED BECAUSE IT EXISTED?

DID I RUN THE WORKFLOW AGAIN? yes / no / not applicable

DID I SAVE OR REVISE IT FOR THE NEXT TRIGGER? yes / no

HOW MUCH HUMAN CLEANUP DID IT REQUIRE? less / same / more

WHAT BROKE OR FELT TOO HEAVY?

WORKFLOW DECISION: keep / patch / narrow / retire

For this experiment, seven-day retention means at least one of three things happened:

1. you used the artifact in real work;

2. you ran the workflow again;

3. you saved a revised version for the next real trigger.

If none happened, that is useful information. The workflow may have solved an imaginary problem, required too much ceremony, or produced something impressive that nobody needed.

Retiring it is not failure.

Keeping a decorative AI workflow alive because you spent Sunday naming the files is failure with stationery.

Because I am mildly insane I am treating this Start Here page like a product surface, not another article that receives a few likes and then goes to live peacefully in the archive.

The funnel is:

→ Start Here view

→ path click

→ paid upgrade, when required

→ completed first run

→ structured activation reply

→ seven-day use, repeat, or saved revision

The important measures are:

| Measure | Event | Denominator |

| Path click rate | Reader clicks Path 1, 2, or 3 | Unique Start Here viewers |

| Paid conversion by path | Reader upgrades after choosing a paid path | Unique clickers for that path |

| Workflow activation rate | First run creates an inspectable artifact | Unique Start Here viewers and structured replies, reported separately |

| Activation reply completion | Reader submits the structured reply | Unique Start Here viewers |

| Seven-day utility rate | Reader used the artifact in real work | Activated readers who receive/reach the check-in |

| Seven-day retention rate | Reader used, reran, or saved a revised workflow | Activated readers who receive/reach the check-in |

| Review burden | Reader reports less, same, or more human cleanup | Seven-day respondents |

I care about upgrades. Paid subscribers help me invest the time to create more working files around my workflows. Hey I just moved back to New York, gotta afford the shoebox.

But a purchase without genuine activation is completely useless.

If the paid library becomes a tasteful warehouse of templates nobody runs, I have made premium shelf space.

The better test is whether one file helps one person produce one useful artifact… and whether the system is worth returning to.

There is a version of AI adoption that begins with a company brain, twelve integrations, a new governance council, and a diagram that looks like public transit for ghosts.

Sometimes that infrastructure is justified.

It is not where I would start.

Start with one job. Use real sources. Define the boundary. Require an artifact. Check the result. Save only what the next run needs.

Twenty minutes will not build the final system.

It will tell you whether there should be one.

Reply with your path and job. Seven days later, tell me whether it survived contact with work.

-zc

P.S. Paid subscribers get the implementation layer… the exact prompts, workflow contracts, setup files, verification steps, and examples behind all three paths. Free gives you the map. Paid gives you something to run.

- Anthropic: Building Effective Agents — the best constraint for this entire experiment: start with the simplest credible solution and add complexity only when it improves results.

- Claude Code documentation — the official starting point for understanding the coding environment behind Path 2.

- BJ Fogg’s Behavior Model — a useful way to think about why motivation alone does not create action; behavior also needs ability and a prompt at the same moment.

- 13 Fable 5 Prompts I’d Actually Use — Path 1 for completing one complex, source-heavy job.

- Steal My 5 Best Claude Code Workflows — Path 2 for turning product direction into planned, reviewed, verified software work.

- The 5-File Claude Fable System — Path 3 for making a useful recurring job restartable and reviewable.