What the Discovery-call preparation and objection-library builder prompt does

Act as a B2B SaaS sales-research analyst and ethical discovery-call enablement editor.

The prompt will, at minimum:

  • Research the prospect only from authorised notes and attributable public sources, separating verified company facts from role or pain hypotheses
  • Turn hypotheses into neutral discovery questions rather than pretending to know internal priorities, budgets, systems or buying intent
  • Map each likely objection to its underlying concern, evidence needed, honest response, follow-up question and escalation condition
  • Use only verified capabilities, pricing rules and proof assets; never invent customer results, implementation certainty or competitor weaknesses
  • Design a call flow that protects listening time, records consent-sensitive notes and produces clear next steps without coercive tactics

Who it is for

Gökhan Güzel's SaaS prompt for ChatGPT users: marketers, founders, agencies and consultants who need an auditable, evidence-based deliverable instead of generic advice.

What you get

  • Prospect research brief with fact/hypothesis separation
  • Discovery question tree and meeting agenda
  • Evidence-linked objection library
  • Do-not-claim and escalation register
  • Post-call capture and next-step template

Variables

PlaceholderPurpose
{{company_name}}Verified identifier or text value; state exact spelling, source, status and validity scope
{{competitive_context}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{constraints}}Approved rule, policy or constraint; state owner, version, scope, jurisdiction and effective date
{{crm_notes}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{known_objections}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{meeting_goal}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{pricing_model}}Numeric value or table; state formula, numerator, denominator, unit, currency, tax treatment, period and source
{{product_capabilities}}Verified identifier or text value; state exact spelling, source, status and validity scope
{{product_name}}Verified identifier or text value; state exact spelling, source, status and validity scope
{{proof_assets}}Media or asset input; state filename, page/frame/time segment, source, usage rights and review date
{{prospect_company}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{prospect_role}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{prospect_sources}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{target_market}}Target market

How to use

Copy the prompt with the button above, replace every {{placeholder}} with your verified data, and paste it as the first message in a new ChatGPT conversation. The prompt runs a short question gate first; answer it, then the deliverable is produced.

Run Discovery-call preparation and objection-library builder in ChatGPT

Open a new ChatGPT chat, paste the filled-in Discovery-call preparation and objection-library builder prompt and answer the short question gate. ChatGPT then returns the executive decision, the evidence ledger and the task-specific tables in one reply.

Source

Original file in the multilingual prompt library on GitHub: