AI engineering

Make the workflow inspectable

Adding a model changes more than the way a first draft is produced. Someone needs to define the input, inspect the output, handle unsuccessful requests, and maintain the surrounding code. These guides focus on those decisions in a low-code setting. They start with a concrete task and treat model choice, API behavior, and editor context as parts of one maintainable workflow.

The local-versus-frontier comparison separates deployment location from capability and shows how to evaluate actual task results. The API patterns article explains how to define output contracts and think through retries before a network request changes an external system. The AI IDE guide shows how planning, small code changes, and explicit verification fit together when working with an assistant. Use these articles as a set of questions for your own project. Keep results, failures, and accepted changes visible so that a future editor can understand how the workflow reached its current state.

• Evaluate models on representative project tasks.
• Keep private credentials outside public website files.
• Review small changes and record why they were accepted.

For the broader context, explore the ai llm evaluation guide.