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Hey builders,
A polished AI paragraph can still leave you with the worst kind of work: deciding which sentence is a fact, which one is a guess, and which one you need to check. Today’s skill is simple: ask for an answer contract before you ask for an answer.
Start here: save assets/ANSWER-CONTRACT-CARD.md, then run Blueprint I with a harmless, small source such as meeting notes you wrote or a public article. It works in any free chat. No API key, paid model, upload, agent connection or special JSON mode is required.
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Blu Dot surpasses 2,000% ROAS with self-serve CTV ads
Home furniture brand Blu Dot blew up on CTV with help from Roku Ads Manager. Here’s how:
After a test campaign reached 211,000 households and achieved 1,010% ROAS, the brand went all in to promote its annual sales event. It removed age and income constraints to expand reach and shifted budget to custom audiences and retargeting, where intent was strongest.
The results speak for themselves. As Blu Dot increased their investment by 10x, ROAS jumped to 2,308% and more page-view conversions surpassed 50,000.
“For CTV campaigns, Roku has been a top performer,” said Claire Folkestad, Paid Media Strategist, Blu Dot. “Comping to our other platforms, we have seen really strong ROAS… and highly efficient CPMs, lower than any other CTV partner we've worked with.”
Using Roku Ads Manager, the campaign moved from a pilot to a permanent performance engine for the brand.
📋 THE BLUEPRINT
Starter: turn a vague summary into a checkable table
Turn the supplied source into a decision table. Do not use outside knowledge.
QUESTION I NEED ANSWERED: [QUESTION]
SOURCE: [PASTE A SMALL, SAFE EXCERPT]
Return a Markdown table with exactly these columns:
ITEM | CLAIM | EXACT EVIDENCE | CONFIDENCE | NEXT HUMAN CHECK
Rules:
- One row per distinct item, claim or option.
- EXACT EVIDENCE must be a short verbatim quote from SOURCE, or the word MISSING.
- CONFIDENCE must be HIGH, MEDIUM or LOW based only on how directly SOURCE supports the claim.
- NEXT HUMAN CHECK must be a specific question, source or action; use NONE only when the quote fully supports the claim.
- If the source does not answer my question, say NOT ENOUGH SOURCE before the table.
- Do not invent names, dates, numbers, citations or conclusions.Why it works: A summary can blur evidence and interpretation. Fixed columns force the model to show its receipt—or admit it has none—while you still get an answer you can scan.
📋 THE BLUEPRINT II
Upgrade: define an output contract before a repeatable extraction
You are extracting decision-ready records from supplied text. Do not use outside knowledge.
TASK: [WHAT I NEED TO DECIDE OR TRACK]
SOURCE: [SMALL, SAFE SOURCE TEXT]
Return exactly one JSON object and no code fences, using this shape:
{
"records": [
{
"item": "string",
"claim": "string",
"evidence": "short exact quote from SOURCE or null",
"evidence_status": "supported | partial | missing",
"confidence": "high | medium | low",
"next_human_check": "specific check or null"
}
],
"unknowns": ["questions SOURCE cannot answer"]
}
Acceptance rules:
- Every record has every field.
- evidence is a direct quote from SOURCE or null; never a made-up citation.
- Use "missing" and a non-null next_human_check when evidence is null.
- Put disagreements, ambiguity and omitted facts in unknowns.
- If this contract cannot be met, return {"contract_failed":"one short reason"} instead of a prose answer.Why it works: The field names are a promise you can inspect. The contract does not make the model truthful, but it makes missing proof and loose confidence harder to hide in a friendly paragraph.
📊 BEFORE / AFTER
Weak prompt: “Summarize these customer interviews and tell me what to build.”
That can produce a confident story with no way to separate a repeated request from one loud opinion. You still have to rebuild the evidence trail by hand.
Lab version: “Create one record per request; quote the exact evidence; mark unsupported claims missing; list what I must check next.”
Now each row has a job: describe, support or escalate. Valid-looking JSON or a neat table is not proof the claim is true. Open the quoted source, sample a few rows, and decide whether the contract caught the uncertainty you care about.
🚧 COMMON MISTAKES
Making fields poetic instead of testable. “Insight” and “vibe” cannot be checked. Prefer
claim,evidence,statusandnext_human_check.Letting the model source itself. If a quote is not in the supplied text, it is not evidence. Ask for
MISSING, not a plausible citation.Treating clean structure as clean data. A model can put a bad inference in a perfect field. Verify a sample and use a real validator when software depends on the result.
Learn the rules behind a reliable JSON record. JSON Schema’s starter guide is a free walkthrough of fields, types and constraints. Grab it →
📐 ONE TERM A DAY
Today’s term: schema
Plain English: A schema is the labeled tray in a toolbox. It says which compartment exists, what belongs there, and what counts as an empty slot.
Why you should care: A clear schema lets people and software spot a missing answer before it travels into a sheet, workflow or decision.
🎁 FREE SUPPLY DROP
1,000 Powerful Prompts for Claude AI — a prompt pack covering writing, coding, research and business work, so you stop starting from a blank box.
💬 Reply FIELDS with the one column you wish every AI answer had.
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