Building AI Products
Source-grounded pack with Plan an AI Feature Launch, Review AI Product Risk and Trust, and Qualify an AI Product Use Case, plus 1 more.
Get this packGrounded in the complete Building AI Products series.
Open the pack, choose a workflow, and follow its setup and input instructions. You can also open each workflow below.
10 source essays · 4 usable skills · reviewed Jul 2026
Use this pack when
Choose this pack when the work touches Building AI Products and you need a bounded workflow with explicit inputs, a defined output, and human review gates.
Choose the job in front of you.
4 source-grounded skills. Load one workflow at a time.
Plan an AI Feature Launch
Use this workflow when choosing an AI product stack and preparing a limited or full release with model strategy, data and learning rights, economics, metrics, enterprise controls, support operations, rollout gates, rollback, and durable ownership.
Output specification
Return an ai_feature_launch_plan with stack_decision, fallbacks, data_rights, learning_loop, metrics, economics, enterprise_controls, support_model, rollout, release_gates, stop_conditions, rollback, owners, and durability.
Review AI Product Risk and Trust
Use this workflow when designing or auditing an AI product's uncertainty, trust, correction, autonomy, permission, data-rights, admin-control, and recovery surfaces before exposing users to model failures.
Output specification
Return an ai_product_risk_review with failure_inventory, trust_states, evidence_ux, correction_controls, autonomy, permissions, data_controls, admin_controls, recovery, support_handoff, blocking_risks, and owners.
Qualify an AI Product Use Case
Use this workflow when deciding whether a proposed AI product feature solves a valuable user job, needs model behavior, fits a real workflow, can absorb uncertainty, has legitimate data, and supports viable economics.
Output specification
Return an ai_use_case_qualification with user_job, product_promise, baseline, workflow_map, value_mechanism, ai_leverage, non_ai_alternative, uncertainty_fit, data_fit, economics, adoption, decision, and proof_criteria.
Plan Model-Behavior Evaluation
Use this workflow when translating an AI product promise into explicit quality requirements, representative gold cases, automatic and human evaluations, regression coverage, release gates, production monitoring, and post-launch ownership.
Output specification
Return a model_behavior_evaluation_plan with product_promise, quality_dimensions, gold_set, automatic_evals, human_evals, critical_cases, rubric, thresholds, release_gates, production_metrics, rollback, owners, and cadence.
Keep consequential decisions explicit.
These workflows structure analysis and artifacts; they do not authorize autonomous external, personnel, financial, legal, security, or other high-impact action.