Originally posted on [Paragraph on April 8, 2026][1].
How to score your AI prompts before they hit the model and why 'input quality' is the layer every AI workflow is missing.
Here's how to find out in 30 seconds using http://pqs.onchainintel.net
I took a prompt that thousands of marketers are using right now:
"Act as a ruthless expert business consultant. Conduct a ruthless assessment of my ecommerce business's biggest marketing weaknesses. Identify 5 specific areas where I am likely losing customers or leaving money on the table. For each, explain the hidden cost, the root cause, and a fix I can implement within 7 days. Be direct and avoid generic suggestions."
Feels . Has a role. Has a task. Has an industry. Sounds like something a senior marketer would write.
Prompt Quality Score analyzed and scored it 16/40.
Here's what the score caught that you didn't:
Specificity 4/10: "ruthless assessment" isn't a deliverable. The model doesn't know what output structure you actually want. It guesses.
Context 3/10: Ecommerce is a category, not a business. No revenue model. No audience size. No current marketing efforts. The model fills every gap with assumptions, and assumptions produce generic output.
Clarity 5/10: "Be direct and avoid generic suggestions" is an instruction about tone, not output. The model still doesn't know what specific looks like for your business.
Predictability 4/10: Run this prompt 10 times, get 10 different answers. Nothing anchors the output to a consistent, repeatable format.
This is the input quality problem. The prompt feels intentional. The score says otherwise.
PQS optimized it. Score jumped to 34/40. +75%.
The optimized version added credibility markers, quantified outcome requirements, a structured diagnostic format, and specific deliverable criteria. Same intent. Completely different architecture.
The output went from a generic 5-point list to a structured revenue audit with quantified impact estimates, strategic root causes, and 7-day implementation steps the model can actually execute against.
What Input Quality Actually Means
Every AI workflow tip you've seen this week, brand voice docs, custom instructions, skill managers, connector integrations, exists for one reason: to build better context for the prompt.
But none of it matters if the prompt itself is poorly structured.
Input quality is the layer before the infrastructure. It's the question nobody has been asking:
Once you've assembled all that context, is the actual prompt any good?
PQS scores it. Pre-flight, not post-hoc. Before the inference call is made. Before the agent executes and asks any model. Before the garbage goes in.
Eight dimensions. Five peer-reviewed frameworks. Seven verticals including Content & Media for marketing prompts specifically.
How To Use It
Go to pqs.onchainintel.net
Select your vertical: Content & Media for marketing prompts
Paste your prompt
Get your score, dimension breakdown, and optimized version
Copy the optimized prompt and fill in your actual business details
Run it
The score tells you exactly what's weak and why. The optimized version shows you what good looks like.
[1]: https://paragraph.com/@*Emails are not allowed*/is-your-ai-prompt-any-good-most-marketers-have-no-idea
