模型 / NAVIN RESEARCH
How to Choose an AI Model for a Production Application
Evaluate quality, latency, cost, context, tool use, deployment, and governance with your own workload.
Evaluate quality, latency, cost, context, tool use, deployment, and governance with your own workload.
简要回答: Choose from a task-specific scorecard, not a universal leaderboard. First eliminate models that fail mandatory privacy, deployment, modality, or tool requirements. Then compare end-to-end task success, p95 latency, and total cost on representative traffic.
实施步骤
- Write hard constraints and quality thresholds before testing.
- Use the same prompts, tools, retrieval data, and retry budget for candidates.
- Measure the full pipeline rather than isolated model calls.
- Plan a fallback and a repeatable model-upgrade process.
验证清单
- Include peak load and rate-limit behavior.
- Test refusals, malformed inputs, and long contexts.
- Record exact model identifiers and evaluation dates.
常见错误
- Picking the highest benchmark average.
- Ignoring operational limits and output-token cost.
- Coupling business logic to one provider's response shape.
实际决策
先交付能够通过上述检查的最小设计。记录测试中使用的模型、提示词、工具、数据集与运行时版本，让后续变化可以被比较而不是被猜测。依赖供应商的限制、模型名称、价格或预览功能之前，请重新检查链接文档；这些细节变化往往快于本文的工程原则。
