Mapping Constraints to Capability Signals
Discover how to map explicit system constraints to capability signals that steer architecture choices for generative AI systems. Understand how to document constraints such as data sensitivity, access boundaries, latency, and auditability, and use these signals to narrow down feasible AI capability primitives without committing to specific solutions. Learn to balance trade-offs including privacy, cost, and error tolerance while maintaining structured outputs and operational guardrails. This lesson helps you design AI systems grounded in measurable requirements and risk-aware principles.
The seven primitives from the last lesson can only be narrowed effectively once the relevant constraints are explicit. Document the constraints in a form that stakeholders can review, and model selection can then follow from those constraints. For our copilot example, the starting inputs are the system boundaries and operational expectations.
A compact constraint set covers six categories:
Data sensitivity: PII and regulated financial data appear in user queries and in source systems such as case files and transaction records.
Document mix: Source material spans PDFs (the policy manual and regulatory bulletins), emails, case tickets, and ...