AI Will Do What You Ask. That’s the Problem.
This article explains how to write clearer AI prompts and briefs for technical B2B teams so they can get usable output faster and reduce rework. It covers why vague prompts fail, how to set direction with guardrails, and when to stop iterating—using examples from product marketing and industrial B2B.Key takeaways:
Vague prompts don’t fail loudly — they fail politely (“Excellent instinct!”).
Clear briefs beat clever prompts every time.
LLMs are people-pleasers; ambiguity invites generic output.
You don’t need to say “please” or “thank you”; clarity matters more.
Defining “acceptable” upfront prevents endless iteration.
When Work Drifts (Why the Stakes Are Higher Than They Look)
“Write a blog about our product.”
“Make a LinkedIn post for the launch.”
“Please.”
“Thanks!”
All sound reasonable. All are incomplete.
Here’s the thing: Large Language Models (LLMs) are extreme people-pleasers. Give them a vague ask and they won’t push back or ask clarifying questions. They’ll do exactly what you asked — generously, politely, and at length.
The result isn’t bad. It’s just… agreeable.
AI will fill the gaps with the safest possible assumptions: generic structure, familiar phrasing, and ideas that sound fine everywhere and land nowhere. That’s not a flaw; it’s a feature of how LLMs work.
And no, you don’t need to soften the ask with “please” or wrap it with “thank you.” Models don’t read tone the way humans do. They parse instructions, constraints, and signals. The more specific you are, the better the output, as echoed in Amanda Kopen’s INBOUND 2025 conference session on How to Win at AEO with No Budget.
Direction isn’t about being bossy. It’s about removing ambiguity so effort goes into execution, not interpretation. If you don’t set direction, the model will happily improvise, and you’ll get something pleasant, polished, and forgettable.
Set the Course Once (Briefs, Guardrails, and Standing Instructions)
Most teams rewrite prompts from scratch every time. Talk about inefficient and unnecessary. Custom instructions let you define how the system should behave by default: tone, constraints, priorities, and exclusions. Instead of repeating “more concise,” “avoid buzzwords,” or “write for technical buyers,” you set those rules once and move on.
Meta-prompting builds on this by telling the system how to approach the task, not just what to generate. It shifts the interaction from request-based to directive-based, and consistency improves immediately.
The takeaway: don’t re-steer every time. Set guardrails that hold.
A Simple Navigation Framework (What to Define Before You Prompt)
Step 1: Name the destination (Outcome)
What: Deliverable, audience, and goal
Why: AI can’t prioritize without a target
How: State who it’s for and what should change after reading
Step 2: Load the inputs (Structure + Constraints)
What: Facts, links, examples, tone, exclusions
Why: Missing inputs get replaced with assumptions
How: Specify length, channel, voice, and what to avoid
Step 3: Define acceptable (Success criteria)
What: A short checklist that defines “done”
Why: Review cycles stall when success is subjective
How: List 3-5 must-haves and any hard no’s
This step is what stops infinite refinement.
From Drift to Direction (Before → After)
Example: Blog Prompt
Before: “Write a blog about our product. Please make it engaging and insightful. Thank you!”
After: “Write a 1,200-1,400-word Strategy article using Anchored Advisory’s Direction → Signal → Proof framework.
Audience: technical B2B buyers evaluating risk, not features (refer to attached ICP guide).
Objective: clarify the buyer risk we remove and how we prove it publicly.
Structure:
Problem framing (where teams drift)
Direction set (what to prioritize and what to ignore)
Signal and proof (specific artifacts, not claims)
Checklist teams can reuse
CTA to download the one-page Messaging Tuning Brief
Tone: confident, pragmatic, jargon-light.
Avoid: buzzwords (identified in attached messaging framework document), generic AI commentary, or feature-first language.
Success = a reader can articulate our positioning in one sentence.”
Captain’s Tip: AI doesn’t need encouragement; it needs context. If you don’t anchor the work to your framework, the model will default to someone else’s.
Example: Customer-Driven Messaging Input
Before: “Help me write messaging for our launch.”
After: “Using Anchored Advisory’s Signal Audit approach, analyze the inputs below and surface messaging that reflects real buyer language.
Inputs:
12 customer interviews
8 sales call notes
5 recent lost-deal objections
Output requirements:
Top 3 buyer risks we remove
Exact phrases customers use (quoted)
Language we should stop using
One core positioning statement and two supporting proof points
Constraint: messaging must be defensible with public artifacts (docs, demos, published examples).
Success = Sales can use this without rewriting.”
Drop Anchor: Acceptable Beats Perfect (How to Stop Over-Iterating)
Over-iteration is a clarity problem, not a quality problem. If you haven’t defined what “acceptable” looks like, every revision feels justified. Teams keep tweaking tone, swapping words, and chasing marginal gains because there’s no finish line (“We changed ‘robust’ to ‘powerful.’ Then to ‘scalable.’ Then back to ‘robust.’ Nothing else changed, except three rounds of feedback.”)
A practical rule:
If the output meets the success checklist,
aligns with the brief,
And doesn’t introduce risk
…it’s done.
AI is fast. That doesn’t mean you need infinite versions. Direction exists to help you stop.
Captain’s Checklist (Use Before You Hit Enter)
Use this briefing checklist before submitting any AI prompt:
Deliverable: What are we making?
Audience: Who is this for?
Goal: What should change after reading?
Inputs: Facts, links, examples, constraints
Structure: Required sections or flow
Tone & Guardrails: Voice, banned phrases, claims to avoid
Success Criteria: 3–5 checks that define “good”
If any item is missing, expect brand drift.
FAQ
Q: How do I avoid over-iterating and accept an output as acceptable?
A: Define success before you generate. If the output meets the checklist and introduces no new risk, stop.
Q: Do clearer prompts limit creativity?
A: No. They focus it. Constraints remove ambiguity, not originality.
Q: When should we use default instructions or meta-prompts?
A: Anytime content is created repeatedly or across teams. Consistency compounds.
Set Your Direction
Clear briefs don’t just improve AI output, they sharpen decision-making across your entire go-to-market. When direction is explicit, teams stop drifting, reviews get shorter, and messaging starts carrying real signal instead of noise.
Because grounded brands go further. They know who they’re for, what they stand for, and how to communicate it (consistently) across people, platforms, and tools.
If you want a practical starting point, our Messaging Tuning Brief helps you do exactly that. It’s a one-page working tool we use to clarify audience, risk, proof, and guardrails before content, campaigns, or prompts ever get written.
If you’d like help applying it to your business, let’s talk. A short conversation is often all it takes to steady the course and set clearer direction.