Design and validation fail when teams pick an approach before they are clear on the workflow, the user, and the definition of “good.” The result is a system that demos well, but cannot be trusted, improved, or scaled.

This step provides a practical structure for turning an AI idea into a product that can be shipped, evaluated, and iterated with measurable quality.

Key points

• Start with the workflow and business objective before choosing technology.
• Choose the simplest viable approach, and be explicit about what you will not build.
• Ship a first version that can be observed in real usage, then iterate.
• Define what “good” means early, and validate quality before scaling.
• Use the substeps to pressure-test GenAI fit, RAG tradeoffs, and cost.

Step 2 is about making product decisions explicit early: workflow, boundaries, success criteria, and validation. When those are clear, building becomes a repeatable engineering process instead of a leap of faith.