November 2026 Summaries

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Choosing an LLM for an AI application should be based on application-specific requirements rather than public benchmark rankings, with candidates evaluated on representative tasks, inputs, failure modes, operational constraints, and user outcomes. The framework recommends defining required tasks, output formats, safety and privacy limits, latency targets, costs, deployment needs, and unacceptable failures before building a test set that includes normal requests, known incidents, edge cases, adversarial prompts, and complete tool-using workflows. Candidates should be tested through an identical evaluation harness with controlled prompts, retrieval, tools, settings, and metadata, then assessed using task-appropriate measures for quality, safety, reliability, structured output validity, robustness, latency, cost per successful task, and operational readiness. Hard gates for critical requirements such as safety, schema validity, data handling, and p95 latency should eliminate unsuitable models before weighted scoring is used to expose remaining trade-offs. The process should also inspect individual failures, test multi-turn and tool behavior, consider routing across multiple models only when validated, and confirm results through shadow deployments or limited production rollouts. Model selection is presented as an ongoing engineering practice requiring versioned datasets, reproducible records, regression testing, and reevaluation whenever models, providers, prompts, retrieval systems, tools, policies, or production conditions change.