Define the task
Describe the user task, expected output and acceptable failure behavior before choosing a model or provider.
AI engineering
Build AI features as systems, not prompt demos
Production AI engineering combines software boundaries, data handling, retrieval, tool permissions, evaluation, fallback behavior and operations. Model choice matters, but the surrounding product and engineering controls determine whether the feature is understandable and supportable.
Separate model from product logic
Keep permissions, business rules, validation and important decisions in application code rather than implicit prompt behavior.
Classify data
Know which inputs are public, internal, personal, confidential or secret before deciding where they may be processed or logged.
Design retrieval deliberately
When external knowledge is needed, define source ownership, access filtering, freshness, citation behavior and no-answer behavior.
Constrain tools
Give AI-enabled workflows least-privilege access to APIs and require explicit validation or approval for consequential actions.
Evaluate representative cases
Use real task examples, edge cases and known failure categories instead of judging quality from a few ideal demonstrations.
Plan fallback behavior
Define what happens when a provider is unavailable, a response is invalid, retrieval finds nothing or a human needs to take over.
Observe the workflow
Measure useful operational signals such as latency, errors, review outcomes and task-level quality without collecting unnecessary sensitive content.
This guidance does not guarantee model accuracy, safety, privacy, regulatory suitability or fitness for a high-impact use. Those questions depend on the real workflow, data and deployment.
Let’s work together
Planning a production AI feature?
Describe the user task, data, integrations, human review, output impact and what success or failure should look like in real use.