Building your first AI product

What to Build

Not every idea is a good AI product, and not every AI product needs the API. The four filters that separate durable use cases from expensive demos — before you commit to a stack.

AI Augmentation

Before you build for customers, get your own house in order. How to configure Claude as a structured OS for a one-person company — not just another tab you open.

AI Use Case Discovery

Claude will cheerfully validate a terrible idea if you ask it the wrong way. The discipline required to use it as a genuine stress-test — not a mirror.

System Prompt

The highest-leverage technical decision in an AI product is not the model — it is the system prompt. What goes wrong when founders get this wrong.

Build vs. Buy

Claude.ai, the API, or a fine-tuned model? Most early-stage founders overcomplicate this. The decision framework, and why fine-tuning is almost never the right first move.

Hallucination

The demo gap, hallucination in production, scope creep, the retention cliff — the specific failure modes that catch AI founders off guard, and how to design around them.

Evals

You cannot improve what you have not defined. How to build a lightweight eval system that tells you whether your product is getting better or quietly degrading.

Deployment

Vercel, Railway, or a VPS — the tradeoffs, the env var discipline, the logging setup, and the first 48 hours after launch. What to watch and what can wait.

Error Handling

Rate limits, overloaded errors, context window exceeded, unexpected refusals — the specific failures your first real users will trigger, and how to handle them gracefully.

AI Strategy

What investors actually want to hear when you pitch an AI product — and why most AI pitches get the narrative wrong by leading with the technology.