The generative AI engineering lifecycle

Generative AI engineering combines data preparation, model selection, prompt and application design, evaluation, deployment, and ongoing monitoring. A model response is only one component of the product. A reliable workflow starts with a user problem and acceptance criteria, builds a small representative dataset, compares an appropriate baseline, and tests likely failure cases before release. On Databricks, these activities can use governed data, notebooks and jobs, model endpoints, experiment tracking, and application monitoring. Choose only the platform components the workload needs. For example, a searchable policy assistant may need retrieval and access controls, while a short text classifier may be better served by a simpler model endpoint. A practical lifecycle begins with discovery: interview users, inspect the workflow, and record constraints such as allowed data, response time, and the cost of a wrong answer. Build a small baseline before adding retrieval or agent behavior. During implementation, version the prompt, model, source snapshot, and application code so an evaluation can be reproduced. Before rollout, test expected use, boundary cases, unsupported requests, and dependency outages. After launch, review observed failures and feed confirmed examples back into the evaluation set. This turns a one-time model demo into a controlled product improvement loop. Implementation drill: trace one representative request through the stages in the diagram. Write down what enters each stage, what output or evidence it produces, and which identity is allowed to perform it. Then test an empty input, an unauthorized request, a dependency timeout, and an unexpected result. For each case, state whether the application should retry, reject, abstain, or ask a person for help. Keep the observed failure as a regression example so changes to prompts, models, data, or code can be checked against the same behavior. Practice task: What should be defined before selecting a model?