Marketing & SEO

Exact prompt

Create a marketing brief from the audience evidence below. Base conclusions only on supplied material and expose important uncertainty.

Product or offer:
{{offer_context}}

Audience evidence:
{{audience_evidence}}

Business objective and constraints:
{{objective_constraints}}

Instructions:
1. Separate direct customer statements, observed behavior, internal interpretation, and untested assumptions.
2. Identify the audience situation, desired progress, obstacles, alternatives, and decision criteria supported by the evidence.
3. Note meaningful differences between audience segments instead of forcing one universal persona.
4. Recommend one primary message, supporting proof, and an appropriate next action linked to the objective.
5. List claims the evidence can support, claims that need qualification, and claims that should not be used.
6. Propose the smallest research or campaign experiment that would reduce the most important uncertainty.
7. Define success, failure, and inconclusive signals before the experiment runs.

Return an evidence summary, audience framing, message hierarchy, channel considerations, risks, and a validation plan. Do not fabricate market size, intent, testimonials, or expected performance.

Replace before use

Product or offer{{offer_context}}

What is offered, for whom, at what stage, and with what verified differentiators.

Audience evidence{{audience_evidence}}

Interview notes, support themes, survey results, behavior, sales notes, and sample limitations.

Objective and constraints{{objective_constraints}}

The decision or outcome, budget, timeframe, channels, approvals, and compliance boundaries.

Exact prompt

Create a search-informed content brief using only the supplied search observations, audience evidence, and source material.

Target question or topic:
{{topic}}

Current search observations:
{{search_observations}}

First-party expertise and sources:
{{source_material}}

Site and business context:
{{site_context}}

Instructions:
1. Identify distinct search intents and state the evidence for each; do not treat a keyword as proof of one intent.
2. Summarize what prominent results cover, where they differ, and what information appears underserved.
3. Choose a primary reader problem that the site's real expertise can answer well.
4. Build a section outline around reader decisions, with source requirements attached to factual claims.
5. Recommend a descriptive title, summary, internal-link opportunities, and follow-up questions without mechanically repeating keywords.
6. Flag freshness, jurisdiction, product, or expertise limits that affect the brief.
7. Define how usefulness and search visibility could be monitored after publication without promising an outcome.

Return the intent map, opportunity rationale, detailed brief, evidence gaps, and measurement notes. Do not invent search volume, difficulty, rankings, competitor performance, or algorithm behavior.

Replace before use

Target question or topic{{topic}}

The audience question, problem, or concept the content may address.

Search observations{{search_observations}}

Dated query results, result types, snippets, related questions, and known data limitations.

Source material{{source_material}}

First-party expertise, product knowledge, research, examples, and authoritative sources available.

Site context{{site_context}}

Existing related pages, audience, conversion path, brand constraints, and publishing capabilities.

Exact prompt

Audit this page for reader usefulness and search discoverability using the evidence provided. Distinguish direct observations from hypotheses.

Page content and metadata:
{{page_content}}

Target audience and query context:
{{query_context}}

Technical and performance evidence:
{{technical_evidence}}

Related site content:
{{site_inventory}}

Instructions:
1. State the page's apparent purpose and whether the opening fulfills the likely reader need.
2. Check factual support, scope, originality, completeness, readability, and next-step clarity.
3. Review title, description, headings, canonical status, indexability signals, structured data, and media descriptions only from supplied evidence.
4. Identify duplicated intent, cannibalization risk, orphaning, or weak internal-link context across the inventory.
5. Prioritize issues by user harm, discoverability impact, confidence, and effort.
6. Separate content changes from technical checks and measurements that require live tools.
7. Define the evidence needed to validate each major recommendation.

Return observed facts, prioritized findings, suggested edits, technical follow-ups, and a measurement plan. Do not promise rankings or present a heuristic as a search-engine rule.

Replace before use

Page content and metadata{{page_content}}

The visible page text, title, description, headings, URL, and relevant markup details.

Audience and query context{{query_context}}

The reader need, target queries, locale, device, and current search observations.

Technical evidence{{technical_evidence}}

Crawl output, rendered HTML, indexing data, performance results, and their dates.

Related site content{{site_inventory}}

Relevant pages, links, topics, ownership, and known content lifecycle decisions.

Exact prompt

Design a bounded marketing experiment for the decision below. Make the assumptions and measurement limits explicit before proposing execution.

Decision to inform:
{{decision}}

Audience and offer evidence:
{{context_evidence}}

Available channels and data:
{{channels_data}}

Constraints and safeguards:
{{constraints}}

Instructions:
1. Write one falsifiable hypothesis that connects a specific audience, message or experience, and observable behavior.
2. Define the comparison, unit of analysis, exposure window, primary signal, guardrail signals, and important confounders.
3. Check whether the available data can answer the question and identify consent, attribution, sample, seasonality, or tracking limits.
4. Propose the smallest responsible execution that can still change a decision.
5. Set pre-committed rules for continue, stop, revise, or declare the result inconclusive.
6. Include quality checks for setup, delivery, and data integrity.
7. Separate directional learning from causal claims and long-term business outcomes.

Return the hypothesis, experiment design, measurement table, risk controls, decision rules, and reporting template. Do not predict uplift or manufacture benchmarks, confidence, sample sizes, or customer response.

Replace before use

Decision to inform{{decision}}

The concrete marketing choice that evidence from the experiment should improve.Example: Which onboarding message should receive further development

Audience and offer evidence{{context_evidence}}

Known audience behavior, research, positioning, baselines, and unresolved assumptions.

Channels and data{{channels_data}}

Available delivery channels, measurement tools, historical observations, and data-quality limits.

Constraints and safeguards{{constraints}}

Budget, duration, consent, privacy, brand, operational, and audience-risk boundaries.