Shipping AI Features Fast (Without Breaking Production)

Why AI Features Fail in Production

Most AI ideas die after the first demo. The prototype looks cool, but it's flaky, slow, and impossible to monitor. In production you need predictability, not magic.

Over the last few projects (Sehatdastak, MoveDial, multiple AI tools), I've settled on a simple flow that takes an AI idea from prompt in Notion to stable feature.

My 5-Step Flow

1. Clarify the decision

What is the model deciding or producing? A summary, a classification, a suggestion, a piece of text?

I write this down as a single sentence. If I can’t describe it clearly, the feature isn’t ready for prompts yet.

2. Freeze the contract

I design the input/output contract as if it were a normal API:

• Input shape (fields, types, required/optional)
• Output shape (JSON with clear keys)
• Error cases and fallbacks

The model is one implementation detail inside this contract, not the contract itself.

3. Prompt as code, not as notes

I keep prompts versioned next to code. No copy–paste from ChatGPT tabs. Prompts are:

• Short
• Structured (sections, bullet points)
• Contain examples from real data

When product requirements change, I update the prompt in Git, not in my head.

4. Guardrails and validation

• JSON Schema or Zod to validate responses
• Retry with a stricter prompt when parsing fails
• Hard business rules applied after the model

If the model output doesn’t pass validation, I treat it like any other failing dependency.

5. Telemetry from day one

• Log inputs (anonymized) and outputs
• Track latency, token usage, and failure rate
• Build a small review UI for rejected/flagged cases

Takeaways

Treat AI features like any other backend: contracts, validation, logging, and rollbacks. The “AI” part is just one step in the pipeline.