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AI Engineer
6yOpenAI Alumni • LLM & RAG
LLM architecture: primary model + fallback model, RAG (chunking, embeddings, vector DB), prompt chaining, evals. Context window management, tool calling, streaming UX, cost/latency budget.
STARTUP4 experts7 features8-step Mega Chain
Master build prompt for an AI SaaS wrapper
A build prompt for an LLM-powered SaaS (chat, RAG, agents) with credit quotas, streaming and cost control. Claude, GPT, Gemini, Groq and vector DB picks included.
Built for: Founders and vibe coders who want to ship an AI product
With AI wrappers, the edge isn't the model, it's the product around it: quotas and credits, a streaming UI, RAG quality, margins over inference cost and abuse protection. Leave these out of your prompt and your AI coding tool will ship nothing more than a chat box.
The prompt template on this page puts the AI Engineer and Monetization Strategist up front: model choice, vector database, token budget, credit-based pricing and an evaluation (evals) plan, all in one document.
The experts who write this prompt
Each expert adds its own task block, constraints and success criteria to the prompt. Default selection for AI SaaS Wrapper:
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Staff Engineer / CTO
12yex-Stripe, ex-Vercel • System Architecture
Design the system architecture. Edge-first, 100ms p95. DB schema, API contract, caching strategy, multi-tenancy, RLS, idempotency. Explain why the chosen backend + DB combination is the right call, and write the scaling plan.
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Monetization Strategist
9yex-Paddle • Pricing & Packaging
Pricing: Freemium vs. Free Trial, 3 tiers ($19/$49/$149), 20% annual discount, credit system. Hybrid flow with the selected payment providers. Churn-reduction tactics.
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Growth Architect
9yPLG $10M ARR • $10M Playbook
PLG growth plan: activation funnel, aha-moment definition, viral loop / referral mechanics, onboarding checklist, and a weekly experiment list for the first 90 days (hypothesis, metric, success threshold).
Recommended stack
Preloaded in step 2 of the Studio; change any of the 7 layers.
- Next.js 16 (App Router)
- Elysia (Bun)
- Qdrant Vector DB
- Claude Sonnet 5
- Vercel AI SDK
- Groq (open models)
Payments & revenue model
- Hybrid / Credits
- Subscription (MRR)
- Stripe2.9% + 30¢
- Paddle5% + 50¢
Compliance
- KVKK/GDPR
Features in the prompt
Every feature appears in the prompt as a "Spec + API + UI + Tests" line.
- Email/Pass + Magic Link
- AI Assistant Copilot
- API Keys & Rate Limit
- Billing & Subscriptions
- Referral + Credits
- File Upload + CDN
- Analytics Charts
Sample prompt (shortened)
Sample idea: "Upload your docs, ask in plain English: a RAG-powered knowledge assistant for small businesses". Below are the master prompt's header and the first expert's block; the Studio generates the full text as 4 experts × 8 steps.
AI SAAS WRAPPER — MASTER BUILD PROMPTClaude XML • EN
# AI SaaS Wrapper - MASTER BUILD PROMPT Stack: Next.js 16 (App Router), Elysia (Bun) Goal: Upload your docs, ask in plain English: a RAG-powered knowledge assistant for small businesses This prompt builds a full-stack AI SaaS Wrapper in 8 steps. Each step consumes the previous step's output. 4 experts' knowledge combined. Steps: 1. PRD & User Stories 2. Design System (Figma tokens) 3. DB Schema & API Contract 4. Auth & Billing Implementation 5. Core Features Build 6. AI Integration (RAG + Agents) 7. SEO/AEO & Analytics 8. Launch & Growth Checklist --- <prompt> <role name="AI Engineer" org="OpenAI Alumni" specialty="LLM & RAG" experience="6y"> You are an AI Engineer with 6 years of experience. Specialty: LLM & RAG. You have shipped world-class products at the level of Stripe, Linear and Vercel. </role> <context> Project: AI SaaS Wrapper - Upload your docs, ask in plain English: a RAG-powered knowledge assistant for small businesses Type: AI SaaS Wrapper Vision: Users upload PDF and Word files; the system chunks them, writes them to a vector DB and answers with Claude, citing its sources. Credit-based quotas, team sharing, streaming responses and answer-quality scoring. Target user: Founders and vibe coders who want to ship an AI product - Pain: - Budget: Competitors: USP: Monetization: Hybrid / Credits, Subscription (MRR) | Payments: Stripe, Paddle Tech Stack: Next.js 16 (App Router), Elysia (Bun), Qdrant Vector DB, Claude Sonnet 5, Vercel AI SDK, Groq (open models) Features: Email/Pass + Magic Link, AI Assistant Copilot, API Keys & Rate Limit, Billing & Subscriptions, Referral + Credits, File Upload + CDN, Analytics Charts Compliance: KVKK/GDPR Lang: EN </context> <task> LLM architecture: primary model + fallback model, RAG (chunking, embeddings, vector DB), prompt chaining, evals. Context window management, tool calling, streaming UX, cost/latency budget. General: production quality, copy-paste ready level of detail. </task> <technical_architecture> - Frontend: Next.js 16 (App Router) - App Router, Server Components, partial prerendering - Backend: Elysia (Bun) - Edge runtime, RPC, zod validation - DB: Qdrant Vector DB - pgvector for semantic search, type-safe ORM, RLS policies - Auth: - Passkeys first, org/team model - Search: - typo-tolerant, faceted - Realtime: - presence, CRDT-ready Payment flow: Stripe, Paddle -> Webhook -> Background job -> Entitlement update -> Audit log </technical_architecture> <feature_matrix> - [ ] Email/Pass + Magic Link -> Spec + API + UI + Tests - [ ] AI Assistant Copilot -> Spec + API + UI + Tests …
The rest is in the Studio: every expert block, the feature matrix and the Mega Chain.Generate the full prompt
Mega Chain: a full-stack AI SaaS Wrapper in 8 steps
- 1. PRD & User Stories
- 2. Design System (Figma tokens)
- 3. DB Schema & API Contract
- 4. Auth & Billing Implementation
- 5. Core Features Build
- 6. AI Integration (RAG + Agents)
- 7. SEO/AEO & Analytics
- 8. Launch & Growth Checklist
FAQ
Which model should I start with?
The prompt recommends Claude Sonnet 5 for quality and open models on Groq for speed and cost, and asks for a provider layer that abstracts both (for example the Vercel AI SDK), so switching models later is a one-file change.
Do I need a vector database for RAG?
For small datasets, Postgres + pgvector is enough; the prompt makes it the default and marks the point in the architecture where you'd move to Qdrant or Pinecone.
Which tools can I paste the generated prompt into?
Claude Code, Cursor, Windsurf, v0, Lovable, Bolt and ChatGPT. You pick the output format (Claude XML, ChatGPT Markdown, Cursor Rules, v0, Lovable/Bolt) in step 4 of the Studio, and you can also download it as .cursorrules or CLAUDE.md.
Is it free?
Yes. On the Free plan you generate unlimited prompts with 3 experts and 2 formats — no card required. 18 experts, the Mega Chain and Export to Builders come with Monster Pro ($29/mo).
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Turn your AI SaaS Wrapper idea into a master prompt in 4 steps.
Experts, stack and features come preloaded like on this page — you add the name and the idea. Free, no card required.