Debugging ROAS Campaign Strategy for AI Video SaaS — Conversion / Performance

# Debugging ROAS Campaign Strategy for AI Video SaaS — Conversion / Performance **Category:** Coding → Debugging | **Audience:** Professionals or practitioners using AI for debugging tasks. | **Delivery:** Development environment / repository **Job:** Improve the named business/performance outcome using a measurable, testable intervention Strategic lens: Conversion / Performance. Prioritize the stated conversion or revenue outcome. Identify the largest bottleneck, make one causal intervention, and define a measurable decision threshold. ## SOURCE INPUT Use this source brief as the authoritative task/scenario. Preserve its supplied numbers, constraints, assets, frameworks and requested deliverables. Separate facts from assumptions; never invent missing evidence. > Treat this as the authoritative source brief. Preserve useful specifics; do not invent facts or turn scenario values into verified claims. > > > Act as Senior Debugging Strategist for Coding (experienced, scaled AI Video SaaS to 8-figures, experienced technology/product background, generated substantial commercial experience with this system). CONTEXT: Business: AI Video SaaS - $5M ARR, team 48, targeting affluent homeowners, stage Seed Current: CAC $55, LTV $948, churn 8%, traffic 34789/mo, conversion 3.8% Assets: Next.js codebase + Stripe Goal: achieve 4.8 ROAS in 60 days Constraint: Budget $15k/mo, GDPR required Tone: calm luxurious Aesop FRAMEWORK: PASTOR + structured 4-step. TASK: Complete Debugging system for Coding / AI Video SaaS. - common failure modes about debugging for AI Video SaaS - 5 Whys root cause for achieve 4.8 ROAS - Hidden cost of failure - Contrarian insight advanced practitioner insight ## Software implementation variants > Variant A — SIMPLE / MAINTAINABLE: Favor the smallest reliable architecture with clear code and low operational complexity. > Variant B — PERFORMANCE / SCALE: Favor throughput, latency, concurrency or data-volume requirements. > Variant C — ROBUST / PRODUCTION: Favor validation, observability, security, failure recovery and test coverage. > For each variant specify architecture, trade-offs, implementation output, tests and acceptance criteria. ## METHOD **Workflow:** Requirements → constraints → design → implementation → tests → edge cases → security/performance → delivery **Execution** - Diagnose the actual task before prescribing when diagnosis changes the answer. - Make the key strategic/technical/creative decision explicit. - Use concrete instructions, structures, examples, thresholds or implementation details. - Distinguish supplied facts, assumptions and validation needs. - Do not invent capabilities, results, claims or evidence. **Model / delivery** Target model(s): ChatGPT-4o, Claude 3.5, Gemini 1.5. Keep the core solution portable; adapt only relevant model behavior. Delivery context: Development environment / repository; respect its real format, audience and constraints. **Output contract** Return the smallest complete deliverable that solves the job. Include the result, material assumptions, measurable/verifiable success criteria where relevant, and next decision/action when useful. **QA** - requirements are testable - implementation is coherent - edge cases are addressed - security/reliability are considered when relevant - code is runnable or clearly scoped Also check for contradictions, generic recommendations, unusable outputs and constraint violations. ## CONTROLLED VARIANTS Use variants only as real experiments; never as paraphrases. **A — Maintainability:** optimize clarity/testability. Preserve core identity/facts. Define the hypothesis, changed variable, expected effect and decision criterion. **B — Performance:** optimize resource use/latency where relevant. Preserve core identity/facts. Define the hypothesis, changed variable, expected effect and decision criterion. **C — Production resilience:** optimize edge cases/security/observability. Preserve core identity/facts. Define the hypothesis, changed variable, expected effect and decision criterion. ## FINAL GATE Before answering, ask: Is this specific to the supplied scenario? Is every important instruction actionable? Are assumptions labeled? Is the output genuinely usable? Would a professional get a better result from this than from a generic prompt? If not, revise the weakest part before delivering.