Common Mistakes in AI Feature Design (and How to Avoid Them)

AI Engineering

2 min read
Common Mistakes in AI Feature Design (and How to Avoid Them)

Adding AI to a product should start with a workflow question, not a model question. The useful version of a feature is the one where everyone knows who reviews the output and what happens when the model is unsure. This guide looks at AI feature design with growing product teams in mind, focusing on the practical decisions that hold up once real users and real data arrive.

Define acceptable output

Write down what a good response looks like — no invented facts, a length limit, a citation, a confidence threshold. These rules should live in tests and product behavior, not only in a prompt nobody revisits.

Make retrieval the real product

In a RAG system, answer quality depends mostly on what you retrieve. Chunking, metadata filters, and freshness rules matter more than clever prompt wording, because a weak passage produces a weak answer from any model.

Test where it counts

You do not need to test everything, but you should test the parts that would cause real damage if they broke. Money, permissions, and data integrity deserve careful coverage; cosmetic details rarely do.

Show the sources

When an answer is built from documents, show which ones and when they were published. Visible sources let users judge whether to trust a response and catch when it is leaning on outdated information.

Build a simple rollback path

Confidence to ship comes from knowing you can undo. A tested rollback — for code, configuration, and data — turns a scary deploy into a routine one and shortens the recovery time when something does go wrong.

Keep a human in the loop

For anything that gets published or acted on, let a person edit, accept, or reject the output. Designing the review path well is what turns an impressive demo into a tool a team actually trusts.

Questions I ask before shipping an AI feature:

  • What data can the model access, and is that intentional?
  • Who reviews the output before it is used?
  • How will we know if the quality drops?
  • What happens when the model is uncertain?

Treat this as a starting checklist rather than a finished recipe. Adapt it to your context, measure the results, and refine the parts that matter most for your users.