Expert Messaging Grader
Encode expert judgment into a consistent messaging scorecard and feedback app
- Difficulty
- Advanced
- Time to result
- ~weeks to results
- Steps
- 7
- Confidence
- 98%
Extract the criteria an expert repeatedly uses when reviewing marketing material and convert them into a structured scorecard. The transcript's cybersecurity example evaluates audience fit, stylistic consistency, tense, tone, urgency, factual versus action-oriented intent, clarity, differentiation, proof, and emotional resonance. An AI workflow accepts a document, scores it criterion by criterion, explains each judgment, and recommends prioritized improvements. Packaging the workflow as a small web application lets customers receive a useful first review before consulting the human expert. The key mechanism is not generic prompting; it is the codification, testing, and calibration of domain expertise. Strong implementations use known expert-reviewed documents to compare outputs, expose unsupported conclusions, and refine the rubric before making the grader customer-facing.
Origin
Nate Burke turned his cybersecurity positioning expertise into a vibe-coded application that reviews one-pagers aimed at chief information security officers.
Core principles
- 01Turn tacit expertise into explicit evaluation criteria
- 02Judge communication for a defined audience
- 03Require evidence for every major claim
- 04Separate diagnostic scoring from recommended revisions
- 05Use AI to scale expert review rather than erase expertise
How to run it
- 1
Capture the expert review process
Observe how the expert critiques several real documents and record the questions, standards, and recurring failure modes used. Convert implicit judgments into named criteria.
Pro tip Ask the expert to compare a strong and weak example aloud.
Watch out A generic rubric will produce generic feedback regardless of model quality.
- 2
Define audience and purpose
State who must respond to the document and what action it should cause. Tailor every criterion to that audience and objective.
Pro tip Include the audience's sophistication, objections, and tolerance for technical detail.
Watch out A document cannot be graded meaningfully without knowing whom it serves.
- 3
Build the scorecard
Define each criterion, its scoring scale, and the evidence required for a high score. Include clarity, differentiation, proof, tone, urgency, and emotional impact when relevant.
Pro tip Add examples of acceptable evidence and common false positives.
Watch out Overlapping criteria can double-penalize the same weakness.
- 4
Design the review output
Return an overall score, criterion-level reasoning, quoted evidence from the document, and prioritized revisions. Keep diagnosis separate from rewritten copy.
Pro tip Ask the system to flag uncertainty when the document lacks enough information.
Watch out A single unexplained score is neither teachable nor trustworthy.
- 5
Package the workflow
Create an interface that accepts the source document and renders the structured review. Restrict file handling and retention according to the material's sensitivity.
Pro tip A lightweight upload application can serve as a lead magnet or pre-consulting diagnostic.
Watch out Customer documents may contain confidential security or commercial information.
- 6
Calibrate against expert judgments
Run the grader on documents the expert has already reviewed and compare criterion-level results. Adjust instructions and scoring anchors until discrepancies are explainable.
Pro tip Maintain a small regression set of strong, average, and weak documents.
Watch out Do not launch based on one impressive demonstration.
- 7
Escalate high-value cases
Let the grader provide a useful first pass and route ambiguous or strategic cases to the expert. Use recurring discrepancies to improve the rubric.
Pro tip Show users which findings are automated and which require expert interpretation.
Watch out The app should not imply that a numerical score guarantees market performance.
In the wild
A cybersecurity company uploads a one-page product document. The app evaluates whether the message fits CISOs, maintains consistent style, creates urgency, distinguishes the product, supports claims with evidence, and evokes an appropriate emotional response. It then recommends the highest-impact revisions.
→ Founders receive a structured first review before engaging the positioning expert more deeply.
A specialist converts a recurring document-review method into an upload tool. Prospects receive criterion-level feedback and can request a human session when the tool identifies strategic ambiguity or weak proof.
→ The specialist scales initial value delivery while preserving expert work for complex decisions.
Common mistakes
Using vague criteria
Instructions such as “make it impactful” do not define what the model should observe or how it should score the document.
Skipping calibration
Without comparison to expert-reviewed examples, apparently polished feedback may encode inconsistent judgments.
Rewriting without diagnosis
Immediate rewrites hide why the original message failed and make expert standards difficult to learn.
Is it for you?
Best for
It is best for experts who repeatedly review one-pagers, landing pages, decks, or sales collateral against stable criteria.
Not ideal for
It is not ideal when the expert cannot articulate the review criteria or when every engagement requires fundamentally different judgment.
From the transcript
“he fed it specific instructions around how to score and evaluate this messaging.”
“He wants to make sure there's good clarity, differentiation, proof.”
“you could even turn this into a product that you monetize or turn something like this into a lead magnet with your customers.”
From the episode
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