Turning a business use case into concrete inputs and outputs for an AI pipeline

Before building a Generative AI application on Databricks, an engineer must translate a vague business goal into a concrete specification of what the pipeline will accept as input and what it must produce as output. This framing drives every later decision: data sources to ingest, retrieval strategy, prompt design, model choice, and evaluation metrics. Getting the input/output contract right early prevents costly rework and misaligned stakeholder expectations.

1 · Learn the must-know

• 1Start by identifying the business stakeholder's success criteria (e.g., accuracy, latency, tone, compliance) and translate them into measurable input/output requirements before selecting tools or models.
• 2Define the expected input format (e.g., free-text question, structured ticket, document upload) and the required output format (e.g., short answer, summary, citation-backed response, JSON) since this determines whether retrieval-augmented generation, agent tool use, or simple prompting is needed.
• 3Identify what proprietary or domain-specific data must ground the outputs, since this determines whether a vector search/retrieval component is needed versus relying on the model's built-in knowledge.
• 4Clarify constraints such as latency, cost per request, throughput, and data governance/PII handling requirements, as these shape architecture choices like model size and chaining complexity.
• 5Determine how outputs will be evaluated (e.g., groundedness, relevance, correctness) so evaluation datasets and metrics can be designed to match the true business objective, not just technical performance.
• 6A common gotcha is over-scoping the use case into a single monolithic prompt; well-designed pipelines decompose complex business goals into discrete, testable input/output stages (e.g., retrieval, generation, formatting).

2 · Check your understanding