Adding AI to your product is easy to demo and hard to ship. The gap between a clever prototype and a feature your users trust is engineering — not magic.

Start from the task, not the hype

Pin down the exact job: which workflow, which decision, which screen. “Add AI” is not a spec. “Draft a reply from this thread” is.

Integrate proven models — do not run a research project

For most product teams, the win is integrating leading LLM APIs (and retrieval over your own data) into a real workflow — not training custom models. Reserve research for when you genuinely need it.

Make accuracy measurable

Define success metrics up front, build an evaluation set, ground answers in your data (RAG), add guardrails and monitoring, and keep a human in the loop where the cost of error is high. Quality should be measured, not assumed.

Examples that ship

• In-product copilots and chat over your knowledge base.
• Smart search, classification and recommendations.
• Workflow automation with AI on the judgment steps.

Pick one concrete use-case, prove it works on your data, then ship it. That is how AI earns its place in your product.