# Full Stack Growth Strategy for Podcast Network
**Category:** Developer Prompt → Full Stack | **Audience:** Professionals or practitioners using AI for full stack tasks. | **Delivery:** Development environment / repository
**Job:** Improve the named business/performance outcome using a measurable, testable intervention.
## 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 Full Stack Strategist for Developer Prompt (experienced, scaled Podcast Network to 8-figures, experienced technology/product background, generated substantial commercial experience with this system). CONTEXT: Business: Podcast Network - $12M ARR, team 47, targeting SMB owners, stage Series A Current: CAC $45, LTV $2209, churn 13%, traffic 21068/mo, conversion 2.3% Assets: Shopify 120 SKUs Goal: grow 100k TikTok in 60 days Constraint: Budget $8k/mo, no dev Tone: bold contrarian Hormozi FRAMEWORK: AIDA+PAS Hybrid + structured 4-step. TASK: Complete Full Stack system for Developer Prompt / Podcast Network. - common failure modes about full stack for Podcast Network - 5 Whys root cause for grow 100k TikTok - Hidden cost of failure - Contrarian insight advanced practitioner insight ## Developer implementation variants
> Variant A — RAPID PROTOTYPE: Optimize for a working proof of concept with minimal moving parts.
> Variant B — PRODUCTION SCALE: Optimize architecture for reliability, performance and maintainability.
> Variant C — HARDENED DELIVERY: Optimize validation, security, observability, testing and failure recovery.
> Each variant must have a distinct engineering objective, not cosmetic code changes.
## METHOD
**Workflow:** Task → inputs/context → constraints → execution method → validation → output schema → error handling
**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**
- inputs/outputs are explicit
- schema is unambiguous
- failure handling is defined
- constraints are testable
- instructions are deterministic where needed
Also check for contradictions, generic recommendations, unusable outputs and constraint violations.
## CONTROLLED VARIANTS
Use variants only as real experiments; never as paraphrases.
**A — Direct execution:** minimal complete instruction path. Preserve core identity/facts. Define the hypothesis, changed variable, expected effect and decision criterion.
**B — Reasoned workflow:** staged reasoning/checkpoints without unnecessary verbosity. Preserve core identity/facts. Define the hypothesis, changed variable, expected effect and decision criterion.
**C — Validation:** schema, edge cases and verification. 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.