AI MVP Playbook
Everything you need to go from idea to shipped AI product in 3 weeks. Architecture decisions, stack picks, LLM integration patterns, and a week-by-week sprint plan.
What's Inside
Why AI MVPs Are Different
The unique constraints and opportunities of shipping with LLMs
Scoping Your AI Product
How to define a 3-week deliverable that actually ships
Choosing Your Stack
Next.js, Supabase, OpenAI, Anthropic — when to use what
LLM Integration Patterns
RAG, function calling, agents, and guardrails
Building the Data Layer
Vector databases, embeddings, and real-time pipelines
AI Evaluation & Quality
How to measure and iterate on LLM outputs
Week-by-Week Sprint Plan
Exact milestones for a 3-week AI MVP sprint
Launch & Post-Launch
Monitoring, bug-fix window, and scaling considerations
Why AI MVPs Are Different
Building an AI product is not the same as building a conventional web app. The outputs are probabilistic, the UX patterns are still being invented, and the infrastructure is evolving at a pace that makes last year's best practice feel dated.
In our experience shipping 15+ AI products across fintech, healthtech, and SaaS, we've identified three core constraints that define an AI MVP:
- 1.Latency is UX. Every 100ms added by your LLM call is felt by the user. Streaming is non-negotiable for chat-like interfaces.
- 2.Evaluation before iteration. Without evals, you're flying blind. Ship a lightweight eval harness on day one.
- 3.Guardrails are a feature. Users will try to break your system. Plan for it in week one, not week eight.
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