From Prompt to Product: What AI Doesn't Do

The Magic Illusion

"Build me a SaaS for X."

Type that into Claude or Cursor, and you'll get code. Real, working code. It feels like magic.

But the gap between "working code" and "working product" is vast — and AI doesn't bridge it. AI generates the easy part. The hard part is everything else.

What Prompt-to-Code Gives You

AI code generation genuinely delivers:

• Syntactically correct code in your chosen language
• Implementation of patterns it's seen before
• Basic UI components that look reasonable
• CRUD operations that work
• Something deployable that runs

This is impressive. It's also about 10% of what a product needs.

The 90% AI Doesn't Handle

Business Logic Edge Cases

AI writes the happy path. But businesses live in edge cases: What happens when payment fails? When users cancel mid-subscription? When data is partially migrated? When two operations conflict?

AI doesn't anticipate these because they require understanding your specific business context. It generates code for the scenario you describe, not the hundred scenarios you didn't.

Integration Reliability

AI will write Stripe integration code. It won't write code that handles Stripe being temporarily down, webhooks arriving out of order, or network timeouts mid-transaction.

Real integrations need retry logic, idempotency, error recovery, and graceful degradation. AI generates the API call; the reliability layer is on you.

Security Hardening

AI generates code that works. Not necessarily code that's secure. Rate limiting, input validation, SQL injection prevention, XSS protection, proper authentication flows — these need intentional implementation, not generation.

Operational Infrastructure

Who's signing up? Where are they getting stuck? What's your conversion rate? AI doesn't add analytics, monitoring, alerting, or any of the operational visibility a real product needs.

Customer Lifecycle

Lead capture, nurturing, conversion, retention — the systems that turn visitors into customers and customers into revenue. AI doesn't know these exist unless you specify them, and if you knew to specify them, you probably wouldn't need AI.

The Expertise Gap

Here's the core problem: AI can only implement what you know to ask for.

If you've never built a SaaS before, you don't know to ask for:

• Webhook handlers that process events idempotently
• Subscription state machines that handle edge cases
• Lead capture that flows into CRM with source tracking
• User models that anticipate teams and organisations

You don't know what you don't know. And AI won't tell you.

Filling the Gap

The gap between prompt and product is filled by:

• Experience: Knowing what to build because you've done it before
• Proven patterns: Starting with infrastructure designed by someone with experience
• Iteration time: Building, breaking, rebuilding until it works

You can gain experience the hard way (months of trial and error), or you can leverage someone else's experience (starting with a production-ready foundation).

AI accelerates coding. But you can't accelerate your way around missing knowledge — you can only borrow it.

AI + Foundation = Product

The winning formula isn't AI alone. It's AI plus a foundation that already contains the 90% you don't know to ask for.

Start with infrastructure that embeds the expertise. Use AI to customise and extend. That's how you go from prompt to actual product, not just prompt to code.