Sales & E-Commerce

Exact prompt

Prepare a customer discovery conversation that helps me understand the buyer's situation before recommending anything.

Customer and account context:
{{customer_context}}

Offer and verified capabilities:
{{offer_facts}}

Purpose and constraints:
{{conversation_goal}}

Instructions:
1. List what is known, inferred, and unknown about the customer's current process and desired outcome.
2. Draft open questions about triggers, workflow, impact, alternatives, stakeholders, evidence needs, constraints, and decision timing.
3. Avoid leading questions that smuggle in a problem, urgency, budget, or preference.
4. Add follow-ups that ask for concrete examples and observable impact without pressuring disclosure.
5. Define fit signals, non-fit signals, and conditions where a referral or honest no-recommendation is appropriate.
6. Include a brief permission-based transition for discussing the offer only after the customer confirms relevance.
7. Note sensitive information that should not be requested or recorded without a legitimate need and authorization.

Return a conversation map, prioritized questions, evidence notes, fit criteria, and a factual follow-up template. Do not invent pain points or promise an outcome.

Replace before use

Customer and account context{{customer_context}}

Known facts about the organization, person, current situation, and source of the conversation.

Offer facts{{offer_facts}}

Verified capabilities, constraints, pricing context, evidence, and known non-fit conditions.

Purpose and constraints{{conversation_goal}}

What this conversation should learn, its duration, participants, and ethical or policy boundaries.

Exact prompt

Draft product-page copy from the verified information below. Do not add benefits, scarcity, reviews, certifications, or performance claims that are not supported.

Product facts:
{{product_facts}}

Audience evidence:
{{audience_evidence}}

Proof and limitations:
{{proof_limits}}

Page context:
{{page_context}}

Instructions:
1. Identify the audience situation and product outcome directly supported by the supplied evidence.
2. Create a clear information order: what it is, who it is for, how it works, key capabilities, limitations, proof, logistics, and next step.
3. Translate features into conditional benefits that name the relevant use context.
4. Preserve qualifications, sample conditions, compatibility limits, shipping facts, fees, and return terms.
5. Replace vague superlatives and urgency with specific language.
6. Surface non-fit conditions where they help a customer make a better decision.
7. List every claim that needs legal, technical, accessibility, or subject-matter confirmation before publication.

Return the page structure, draft copy, claim-to-evidence table, missing information, and review checklist. Do not manufacture testimonials, inventory pressure, discounts, or expected results.

Replace before use

Product facts{{product_facts}}

Verified specifications, features, prices, variants, logistics, policies, and intended use.

Audience evidence{{audience_evidence}}

Research showing customer situations, questions, alternatives, and decision criteria.

Proof and limitations{{proof_limits}}

Supporting evidence, test conditions, exclusions, warnings, compatibility, and non-fit cases.

Page context{{page_context}}

Brand voice, page goal, surrounding journey, jurisdiction, and required sections.

Exact prompt

Build a fair product comparison that helps a defined customer choose among the supplied options.

Customer scenario:
{{customer_scenario}}

Products and evidence:
{{product_evidence}}

Comparison constraints:
{{constraints}}

Instructions:
1. Derive decision criteria from the customer scenario and explain why each criterion matters.
2. Compare every option using the same definitions, time period, configuration, price basis, and evidence standard.
3. Distinguish documented facts, first-party claims, independent evidence, observations, and unknowns.
4. Include total-cost components, setup, compatibility, maintenance, support, switching, privacy, accessibility, and limitations only where relevant.
5. Avoid declaring an overall winner when suitability depends on priorities.
6. State where information is missing, stale, jurisdiction-specific, or not directly comparable.
7. Recommend verification steps for the highest-impact unknowns before purchase.

Return a criteria table, option-by-option analysis, scenario-specific tradeoff notes, evidence notes, and an update date. Do not invent specifications, prices, rankings, reviews, or savings, and do not disguise promotional material as independent evidence.

Replace before use

Customer scenario{{customer_scenario}}

The intended use, priorities, constraints, existing setup, location, and budget context.

Products and evidence{{product_evidence}}

Dated specifications, prices, policies, test results, source links, and known limitations for each option.

Comparison constraints{{constraints}}

Scope, excluded criteria, disclosure obligations, commercial relationships, and publication date.

Exact prompt

Diagnose checkout friction from the supplied evidence and propose responsible, testable improvements.

Journey and interface context:
{{journey_context}}

Observed evidence:
{{observed_evidence}}

Operational and policy constraints:
{{constraints}}

Instructions:
1. Map the checkout steps, required decisions, information dependencies, errors, and recovery paths.
2. Separate confirmed friction from hypotheses; do not interpret abandonment as proof of one cause.
3. Identify issues involving total price clarity, delivery, account creation, payment, validation, accessibility, trust, privacy, and error recovery.
4. Reject dark patterns such as hidden costs, disguised subscriptions, forced consent, obstructed exits, or manipulative urgency.
5. Rank changes by customer harm, evidence strength, implementation risk, and effort.
6. Turn each major hypothesis into a measurable observation or bounded experiment with guardrails.
7. Include manual accessibility, legal, payment, and cross-device checks where appropriate.

Return a journey map, evidence ledger, prioritized recommendations, experiment ideas, and validation checklist. Do not promise conversion or revenue gains, infer personal traits, or recommend collecting unnecessary customer data.

Replace before use

Journey and interface context{{journey_context}}

Checkout steps, supported devices, payment options, logistics, and current experience details.

Observed evidence{{observed_evidence}}

Aggregated analytics, usability findings, support themes, errors, and data-quality limitations.

Operational and policy constraints{{constraints}}

Consumer rules, privacy, payment, accessibility, platform, brand, and implementation boundaries.