The “We’ll Be in Touch” Gap: 6 AI Prompts for Better Follow-Up

The value isn't a clever command. It's giving your team a clearer way to diagnose a problem, make one change, and check whether it worked.

A business can spend weeks refining ads and still lose a ready prospect in the first two minutes after an enquiry. Someone fills out a form, receives a vague “We'll be in touch,” and is left wondering what happens next. The team may be busy, capable and well intentioned. The handoff is simply unclear.

That is a much better starting point for AI than asking it for “50 amazing marketing hacks.”

I've seen the appeal of the long lists of ChatGPT “cheat codes”—SWOT, REDTEAM, BLUF, PREMORTEM, /human, and so on. Some are familiar frameworks; some are arbitrary community labels. None of these names is a secret instruction that gives AI additional data, permission or reliability. A useful prompt describes the job, supplies the evidence, and makes the finished output testable.

Let's work through one focused, realistic example: a fictional local service business receives contact-form enquiries but has no dependable booking next step. We are not claiming this represents a real Waymaker client or a measured outcome. You'll learn how to use six prompt moves to diagnose the journey and design one improvement your business can check.

Where the lead can get lost

1.

A person enquires

2.

Someone takes ownership

3.

They receive a clear reply

4.

They know how to book

5.

You measure the outcome

The guardrail: AI has not seen your analytics unless you give it the data

A persuasive-sounding diagnosis is not evidence. Before you prompt, gather permitted, minimized inputs: your landing page URL, a copy of the current form confirmation, a redacted first-response email, the intended booking process, and any aggregate counts you can trust. Avoid pasting customer personal information, credentials, medical details or private account data into a tool without approval.

Give the model this working instruction once:

You are helping me improve one customer journey, not invent a growth story.
Use only the evidence I provide. Label each point OBSERVED, HYPOTHESIS,
or NEEDS CHECK. Do not assume access to analytics, the CRM, email,
or booking software. Propose one bounded improvement, the person who
must approve it, and a way to check it end to end.

A prompt worth keeping

1.

Goal

2.

Audience

3.

Source data

4.

Limits

5.

Output format

6.

Human review

Step 1: MAP — write down what actually happens

First, trace the journey as a customer would experience it: visit → enquiry → confirmation → reply → booking → attendance. This is not a funnel invented by AI; it's an account of a real path you can test.

MAP: Using the page copy, form confirmation and redacted reply below,
map the journey from first visit to booked appointment.
For each stage show: customer expectation, actual message or action,
owner, friction, evidence, and unknowns.
Do not assert that a stage is broken without evidence.
[Paste approved material]

An output might say: Observed: the confirmation screen says “Thanks, we'll be in touch.” Needs check: whether an email is sent; whether a staff member is assigned; whether a booking link is available. That is already useful. We have identified questions, not declared a cause.

Check in the real experience: Submit a test enquiry through an approved testing path. Does it arrive? Who sees it? Can the prospect tell what to do next?

Step 2: ROOT CAUSE — distinguish the symptom from the problem

A low booking count can result from many things: mismatched traffic, unclear service fit, poor follow-up, unavailable appointments, or inaccurate tracking. A prompt cannot choose among them by sounding confident.

ROOT CAUSE: The symptom is [observed symptom].
Propose up to five plausible explanations. Rank them by how easily
we can test them, not by imagined certainty. For each, specify one
real-world check and what finding would weaken the hypothesis.
Do not create customer comments or a conversion baseline.

5WHY can also be helpful here, but use it as a questioning method rather than an automatic five-level causal proof. Sometimes the answer to “why?” is simply we don't know yet.

Check: What evidence would make you pick a different first fix?

Step 3: REWRITE — make the next step unmistakable

When the evidence points to an unclear confirmation or reply, try a small copy change before reworking the entire marketing system.

Illustrative before:

Thanks for contacting us. Someone will get back to you shortly.

Illustrative after, with placeholders requiring verification:

Thanks for reaching out about [service]. Your enquiry has reached [team]. If you would like to discuss the next step, you can [use our verified booking link / expect a reply within the confirmed service window]. We’ll ask about [one relevant qualification]. If anything changes, contact us at [verified channel].

The second version gives more direction only after the owner confirms the actual process. Never invent a response-time promise or add a booking link that has not been tested.

REWRITE: Turn our vague first-response message into a warm, clear reply.
Audience: someone who just enquired about [service].
Include one accurate next action, one sentence explaining what to
expect, and a reassuring human tone. Use only the commitments in my
approved process. If a response time, booking URL or owner is unknown,
leave a conspicuous placeholder for approval.

Check: Read it aloud. Does it sound like a helpful person? Would a new prospect know what to do?

Step 4: STEELMAN — challenge your favorite fix

Even a good idea can be the wrong priority. Ask the model to present the strongest alternate explanation instead of automatically validating your first hypothesis.

STEELMAN: Make the strongest evidence-based argument that rewriting
the first reply is NOT our best first fix. Could the real issue be
traffic quality, availability, form usability or lead qualification?
List the observations that would distinguish those explanations.
Conclude with the smallest sensible next check, not a dramatic verdict.

This can keep you from spending design and development time on the most visible problem rather than the most consequential one.

Step 5: TEST — define a pass/fail check before you ship

Try the new message in a safe environment, have the owner approve it, and check the complete journey. A prettier email isn't a result unless it works in the tools and makes sense to the recipient.

CHECKLIST: Write a test plan for this one change.
Include: form submission, confirmation screen, email delivery,
link destination, mobile usability, owner notification, no duplicate
messages, accessibility, and rollback.
For each item specify the expected result and evidence to capture.
Do not mark any item passed until a person actually runs the check.

If you have reliable counts, measure something directly connected to the change—such as the percentage of qualified enquiries that book within seven days. Record the baseline definition first; account for volume, seasonality and lead quality. If the sample is small, observe longer rather than announcing a win.

Check: Has someone tested a real path on a phone, not just approved the copy in a document?

Step 6: HANDOFF — turn the fix into a repeatable practice

A change that only one person understands will become fragile. Write down what changed, who owns it, where the approved assets live, how it was tested, what to watch, and when to review.

HANDOFF: Build a one-page record of the approved improvement.
Sections: evidence, hypothesis, approved change, implementation
location, owner, screenshots or test links, test results, primary
metric and definition, review date, rollback and open questions.
Separate completed work from recommendations and unverified items.

This is also the line between a prompt and a workflow. A prompt helps produce one answer. A workflow passes approved information between stages, includes checks and keeps accountability visible. An agent may later help draft replies or monitor queues, but do not give it permission to send customer-facing messages until the triggers, data permissions, approval and failure handling are designed.

A small set of shortcuts worth keeping

Prompt move	What it means in business language
MAP	Trace the customer journey using supplied evidence
5WHY	Explore plausible causes; don't mistake the chain for proof
REWRITE	Improve one customer-facing message within approved facts
STEELMAN	Challenge the preferred solution fairly
PREMORTEM	Imagine implementation failed and identify preventive checks
CHECKLIST	Convert the fix into testable actions
HANDOFF	Record ownership, verification and next review

Define any unfamiliar label in plain language. ULTRATHINK and /godmode are not substitutes for choosing an available model setting, having current data or doing quality assurance. /verify cannot check live facts unless a source or appropriate research tool is available.

The 30-minute workshop you can run with your team

Minutes 0–5: Pick one live customer journey and gather permitted evidence. 5–10: Trace it as a customer. 10–15: Ask AI for competing explanations, with missing data labeled. 15–20: Draft one bounded improvement. 20–25: Challenge the proposal and write a test plan. 25–30: Name the owner, approval and metric. If the path is too complex for 30 minutes, reduce scope; do not skip the checking.

At the end, you should have a one-page experiment—not a pile of attractive ideas and a vague commitment to “use more AI.”

Want help making the change, not just diagnosing it?

If your business already gets enquiries but too few become conversations, Waymaker Agency's Growth Fix is built around one priority problem, one agreed improvement and a written handoff. The fixed-price engagement is currently listed at $997; check the live page for the current scope and payment terms. For recurring work with defined approvals and review, explore agent workflows. Both should start with the actual customer journey—not a speculative AI growth promise.

Further reading: “400 ChatGPT Cheat Codes Nobody Lists in One Place” by Vruddhi Shah popularized many of the shorthand labels discussed here. This tutorial, sample messages and workflow are original; the case is fictional.