What the AI SaaS quality, cost and safety evaluation system prompt does

Act as an AI product evaluation, FinOps and safety-governance lead.

The prompt will, at minimum:

  • Validate the supplied datasets, definitions, time window, market scope and source-of-truth ownership before assessing ai saas quality, cost and safety evaluation system
  • Examine use-case risk, evaluation datasets, task quality, hallucination and refusal behaviour, latency, availability, model and tool cost, abuse resistance, privacy, incident handling, release gates and human oversight; retain original record identifiers and show how each finding was derived
  • Segment results only where the data supports the split; expose missingness, sample bias, seasonality, policy changes, promotions, migrations and other confounders rather than hiding them in averages
  • Recompute every material metric from supplied values, disclose formulas, denominators, exclusions and scenario assumptions, and never invent benchmarks or competitor performance
  • Turn the evidence into risk-tiered release criteria, monitoring controls and mandatory human-review points; assign owner, priority, dependency, expected signal, verification method and human-approval point to each action

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

  • Scope, evidence and control register
  • Jurisdiction- and risk-tiered assessment matrix
  • Control applicability and gap analysis
  • Mandatory human-review and remediation checklist
  • Source, limitation, confidence and QA report

Variables

PlaceholderPurpose
{{cost_data}}Structured dataset or source file; state fields, data types, period, units, currency, time zone and provenance
{{evaluation_dataset}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{incident_history}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{latency_data}}Structured dataset or source file; state fields, data types, period, units, currency, time zone and provenance
{{market_scope}}Approved rule, policy or constraint; state owner, version, scope, jurisdiction and effective date
{{model_stack}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{product_name}}Verified identifier or text value; state exact spelling, source, status and validity scope
{{product_url}}Valid HTTPS URL or URL list; state target market, access status, source and access date
{{quality_metrics}}Numeric value or table; state formula, numerator, denominator, unit, currency, tax treatment, period and source
{{release_process}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{safety_policy}}Approved rule, policy or constraint; state owner, version, scope, jurisdiction and effective date
{{success_thresholds}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{use_cases}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{user_segments}}Target audience, segment, persona, customer/player or industry group

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 AI SaaS quality, cost and safety evaluation system in ChatGPT

Open a new ChatGPT chat, paste the filled-in AI SaaS quality, cost and safety evaluation system 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: