Find the right role for AI in your API workflow

Describe the transformation

An AI feature should have a clear place in the product. Consider an incoming document: your application might classify it, extract selected fields, compare its meaning with a query, or draft a summary. Those are different transformations, even when a single provider offers several of them.

Write one sentence for the proposed behavior: “Given this input, produce this output for this user.” Then ask whether a rules-based method already solves the task well enough. That baseline gives the AI experiment something meaningful to improve.

Make quality observable

NIST’s AI Risk Management Framework treats evaluation and trustworthiness as considerations throughout an AI system’s lifecycle. A practical application is to define success before reviewing impressive demonstrations. Use examples that resemble the data and decisions your own workflow will face.

• Input fit: Include incomplete, noisy, multilingual, or unusually long material when it is relevant.
• Output usefulness: Ask whether a person or downstream system can act on the result.
• Failure cost: Distinguish an awkward sentence from an incorrect decision affecting a user.
• Review path: Identify who can correct, approve, or discard a questionable result.

Track the reason for each failed example. A missing source document calls for a different fix from an unclear instruction or an output parser that rejects valid data.

Design the surrounding workflow

Decide where data enters the system, which fields are necessary, and where results are stored. Restrict the information sent to what the task requires. Define a useful empty state for requests that cannot be completed, and tell the user what they can do next.

Evaluate the whole interaction. A generated answer that arrives after the user has abandoned the page is a poor product outcome, even if its wording is strong. Likewise, a quick answer that requires extensive repair may move work rather than save it.

Use LLM API fundamentals for language-centered requests. If the feature must choose and execute actions, continue to agent workflow design and define those actions separately from text generation.