Choosing an AI system
Choosing an AI system requires workload evidence across quality, latency, cost, privacy, safety, and operational fit. A leaderboard can narrow candidates but cannot replace tests on representative prompts, tools, failure modes, and traffic patterns.
Overview. Choosing an AI system requires workload evidence across quality, latency, cost, privacy, safety, and operational fit. A leaderboard can narrow candidates but cannot replace tests on representative prompts, tools, failure modes, and traffic patterns.
Mechanism and context. Selection is a systems problem: the base model interacts with prompts, retrieval, tools, interfaces, infrastructure, and operating constraints. A candidate that leads on one benchmark can still be a poor fit when the application requires a different task shape or trade-off.
Practical use. Define must-pass cases, unacceptable failures, expected traffic, latency and cost bounds, data handling needs, tool requirements, and a fallback plan before comparing vendors. Run the same representative cases through the complete candidate systems and retain examples behind the summary metrics.
Related reading. Read Model evaluation for test design, How to read a model claim for provenance, Model, product, and platform are different layers for the system map, and the Compare view for a side-by-side evidence record.
Claims and evidence
Claim ledger, 2Choosing an AI system requires workload evidence across quality, latency, cost, privacy, safety, and operational fit.
A leaderboard can narrow candidates but cannot replace tests on representative prompts, tools, failure modes, and traffic patterns.