Model Strategy Starts with a Scoring Matrix
Explore how to develop a scoring matrix that aligns AI model selection with key performance indicators and non-functional requirements. Understand setting thresholds and weights for factors like latency, cost, privacy, and data residency, and learn to incorporate evidence and risk management to create a defensible, review-ready model shortlist that fits enterprise controls and compliance.
The data and context specification settled what the copilot may retrieve, and under what constraints. It says nothing about which model should generate the answer. Express model selection as a scoring matrix tied to KPIs and NFRs: give each dimension a threshold, a weight, and required evidence. Skip that step and selection stays a preference that can’t survive security review, procurement questions, or an incident postmortem. For a copilot using permission-aware retrieval to answer and draft against internal policy and process content, the matrix translates stakeholder outcomes into a defensible short list of model classes.
Align the matrix with the copilot’s data and context contract. Permission-aware retrieval constrains what can be sent to the model, what must be filtered before ...