Over one million businesses around the world are leveraging AI to drive greater efficiency and value creation. But some organizations have struggled to get the results they are expecting. What is causing the gap?At OpenAI we are leveraging AI internally to achieve our ambitious goals. One key set of tools we use areevals, methods to measure and improve the ability of an AI system to meet expectations.全球有超过一百万家企业正在利用人工智能来提升效率并创造更多价值。然而部分组织却难以获得预期的成效。造成这一差距的原因何在在 OpenAI我们也在内部利用人工智能来实现宏大的目标。我们使用的一套关键工具是“评估”evals——即用于衡量并提升人工智能系统满足预期能力的方法。Similar to product requirement documents, evals make fuzzy goals and abstract ideas specific and explicit. Using evals strategically can make a customer-facing product or internal tool more reliable at scale, decrease high-severity errors, protect against downside risk, and give an organization a measurable path to higher ROI.与产品需求文档类似评估evals能将模糊的目标和抽象的构想变得具体且明确。通过策略性地运用评估企业能够提升面向客户的产品或内部工具在大规模应用场景下的可靠性减少严重错误防范潜在风险并为实现更高的投资回报率ROI开辟一条可量化的路径。At OpenAI, our models are our products, so our researchers use rigorous frontier evals(opens in a new window) 1 to measure how well the models perform in different domains. While frontier evals help us ship better models faster, they cannot reveal all the nuances required to ensure the model will perform on a specific workflow in a specific business setting. That is why internal teams have also created dozens ofcontextual evalsdesigned to assess performance within a specific product or internal workflow. It is also why business leaders should learn how to create contextual evals specific to their organization’s needs and operating environment. 、在 OpenAI模型即产品因此我们的研究人员利用严谨的“前沿评估”frontier evals来衡量模型在不同领域的表现。尽管前沿评估有助于我们更高效地推出更优质的模型但它们无法揭示确保模型在特定业务场景及工作流程中表现良好所需的所有细节。正因如此内部团队开发了数十种“情境化评估”contextual evals专门用于评估模型在特定产品或内部工作流程中的表现。这也正是为什么业务领导者应当学习如何根据自身组织的需求与运营环境构建相应的“情境化评估”。This is a primer for business leaders looking to apply evals in their organizations. Contextual evals, each crafted for a specific organization’s workflow or product, are an active area of development and definitive processes have yet to emerge. As a result, this article provides a broad framework that we have seen work across many situations. We expect this field to evolve and for more frameworks to emerge that address specific business contexts and goals. For example, an excellent eval for a cutting-edge, AI-enabled consumer product might require a different process than an eval for an internal automation based around a standard operating procedure. We believe that the framework presented below will serve as a collection of best practices in both cases, and will be a useful guide as you build evals tailored to your organization’s needs.本文旨在为希望在组织内应用评估evals的商业领袖提供入门指南。“情境化评估”——即针对特定组织的工作流程或产品量身定制的评估——目前正处于积极发展阶段尚未形成标准化的流程。因此本文提供了一个在多种场景下均行之有效的通用框架。我们预计该领域将不断演进并涌现出更多针对特定业务背景和目标的评估框架。例如针对前沿AI赋能消费类产品的评估其流程可能与基于标准作业程序SOP的内部自动化系统的评估截然不同。我们相信下文介绍的框架汇集了适用于上述各类场景的最佳实践将为您根据组织需求构建评估体系提供有益的指导。How evals work: Specify → Measure → Improve1. Specify: Define what “great” meansStart with a small, empowered team that can write down the purpose of your AI system in plain terms, for example: “Convert qualified inbound emails into scheduled demos while staying on brand.”This team should be a mix of individuals with technical and domain expertise (in the given example, you’d want sales experts on the team). They should be able to state the most important outcomes to measure, outline the workflow end-to-end, and identify each important decision point your AI system will encounter. For every step in that workflow, the team should define what success looks like and what to avoid. This process will create a mapping of dozens of example inputs (e.g. inbound emails) to the outputs they want the system to produce. The resultinggolden setof examples should be a living, authoritative reference of your most skilled experts’ judgement and taste for what “great” looks like.首先组建一支精干且拥有自主权的小型团队让他们能用通俗易懂的语言阐明AI系统的目标——例如“将符合条件的入站邮件转化为已安排的演示预约同时保持品牌调性一致。”该团队应由具备技术专长和业务领域专长的人员共同组成以前述例子为例团队中应包含销售领域的专家。他们需要能够明确界定关键的衡量指标梳理端到端的业务流程并识别出AI系统将面临的每一个重要决策节点。针对流程中的每一个步骤团队都应明确“成功”的标准以及需要规避的问题。这一过程将建立起从各类输入样本如入站邮件到系统预期输出结果之间的对应关系。由此形成的“黄金样本集”golden set将成为一份动态更新的权威参考体现出你们最资深的专家对于何为“卓越”的判断标准与品味。Do not get overwhelmed with a cold start or try to solve it all at once. The process is iterative and messy. Early prototyping can help immensely. Reviewing 50 to 100 outputs from an early version of the system will uncover how and when your system is failing. This “error analysis” will result in a taxonomy of different errors (and their frequencies) to track as your system improves.This process is not purely technical—it’s cross-functional and centered on defining business goals and desired processes. Technical teams should not be asked in isolation to judge what best serves customers or the needs of other teams like product, sales, or HR. Consequently, domain experts, technical leads, and other key stakeholders should share ownership.面对“冷启动”阶段时切勿因无从下手而感到不知所措也不要试图毕其功于一役。这是一个充满反复与波折的迭代过程而早期的原型设计能提供极大的助益。通过审查系统早期版本生成的 50 到 100 个输出结果你可以发现系统在何种情况下以及何时出现故障。这种“错误分析”将帮助你建立一套错误分类体系并统计各类错误的发生频率以便在系统不断改进的过程中进行持续追踪。这一过程并非纯粹的技术工作而是一项跨职能协作其核心在于明确业务目标与理想流程。不应要求技术团队孤立地判断何种方案最能满足客户需求或是最符合产品、销售或人力资源等其他团队的要求。因此领域专家、技术负责人及其他关键利益相关方应当共同承担责任。2. Measure: Test against real-world conditionsThe next step is to measure. The goal of measurement is to reliably surface concrete examples of how and when the system is failing. To do that, create a dedicated test environment that closely mirrors real-world conditions—not just a demo or prompt playground. Evaluate performance against your golden set and error analysis under the same pressures and edge cases your system will actually face.Rubrics can help bring concreteness to judging outputs from your system, but it is possible to over-emphasize superficial items at the expense of your overall goals. Further, some qualities are difficult or impossible to measure. In some cases, traditional business metrics will be important. In others, you’ll need to invent new metrics. Keep your subject matter experts in the loop throughout, and tightly align the process with your core objectives.接下来是进行评估。评估的目标是可靠地找出系统在何种情况下、以何种方式发生故障的具体实例。为此你需要构建一个高度还原真实应用场景的专用测试环境而不仅仅是简单的演示环境或提示词prompt调试沙盒。请使用“黄金数据集”golden set来评估性能并在系统实际将要面临的压力负载和边界条件下进行错误分析。评估标准有助于量化系统输出的质量但若过度关注表层指标可能会导致偏离整体目标。此外某些质量特征难以甚至无法量化。在某些情况下传统的业务指标至关重要而在另一些情况下则需要制定全新的评估指标。在此过程中务必让领域专家全程参与并确保评估流程与核心目标保持高度一致。To actually test the system, use examples drawn from real-world situations whenever possible, and include or invent edge cases that are rare but costly if mishandled.Some evals can be scaled through the use of anLLM grader, an AI model that grades outputs the same way an expert would; yet, it is still important to keep a human in the loop. Your domain expert needs to regularly audit LLM graders for accuracy and should also directly review logs of your system’s behavior.Evals can help you decide when a system is ready to launch, but they do not stop at launch. You should continuously measure the quality of your systems real outputs generated from real inputs. As with any product, signals from your end-users (whether external or internal) are especially important and should be built into your eval.若要对系统进行实际测试应尽可能采用源自现实场景的案例并纳入或构想一些“边缘情况”——即那些虽不常见但若处理不当便会带来严重后果的特殊情形。部分评估工作可以借助“LLM 评分器”一种能像专家一样评估输出结果的 AI 模型来实现规模化不过让“人在回路”human-in-the-loop中保持参与依然至关重要。领域专家需要定期核查 LLM 评分器的准确性并直接审查系统运行行为的日志。评估不仅能帮助你判断系统何时可以上线其作用更不应止步于上线之时。你需要持续监测系统基于真实输入所产生的实际输出质量。正如对待任何产品一样来自终端用户无论是外部用户还是内部用户的反馈信号尤为重要应当将其纳入评估体系之中。3. Improve: Learn from errorsThe last step is to set up a process for continuous improvement. Addressing problems uncovered by your eval can take on many forms: refining prompts, adjusting data access, updating the eval itself to better reflect your goals, and so forth. As you uncover new types of errors, add them to your error analysis and address them. Each iteration compounds upon the last: new criteria and clearer expectations of system behavior help reveal new edge cases and subtle, stubborn issues to correct.To support this iteration, build a data flywheel. Log inputs, outputs, and outcomes; sample those logson a schedule and automatically route ambiguous or costly cases to expert review.Add these expert judgements to your eval and error analysis, then use them to update prompts, tools, or models. Through this loop you will more clearly define your expectations for the system, align it tighter to those expectations, and identify additional relevant outputs and outcomes to track. Deploying this process at scale yields a large, differentiated, context-specific dataset that is hard to copy—a valuable asset your organization can leverage as you build the best product or process in your market.While evals create a systematic way to improve your AI system, new failure modes can arise. In practice, as models, data, and business goals evolve, evals must also be continuously maintained, expanded, and stress-tested.For external-facing deployments, evals do not replace more traditional A/B tests and product experimentation. They are complements to traditional experimentation that can help guide each other and provide visibility into how changes you make impact real-world performance.最后一步是建立持续改进的流程。针对评估中发现的问题可以采取多种应对方式优化提示词prompts、调整数据访问权限、更新评估标准以更准确地反映目标等等。随着发现新型错误应将其纳入错误分析并加以解决。每一次迭代都建立在前一次的基础上新的评估标准和更明确的系统行为预期有助于揭示新的边界情况edge cases以及那些隐蔽且棘手的问题从而进行针对性修正。为了支持这种迭代应构建一个“数据飞轮”。记录输入、输出及最终结果定期对这些记录进行抽样并将模棱两可或处理成本高昂的案例自动分发给专家进行人工审核。将专家的判断结果整合到评估体系和错误分析中进而利用这些信息更新提示词、工具或模型。通过这一循环您能更清晰地定义对系统的期望使系统更紧密地契合这些期望并识别出更多值得追踪的相关输出与结果。大规模实施这一流程将产生一个庞大、独特且针对特定场景的数据集——这是一种难以被复制的宝贵资产能助力您的组织在市场上打造出卓越的产品或流程。尽管评估体系为改进AI系统提供了一种系统化方法但仍可能出现新的故障模式。在实际应用中随着模型、数据和业务目标的不断演变评估体系也必须持续进行维护、扩展和压力测试。对于面向外部的部署评估体系并不能取代传统的A/B测试和产品实验。它们是对传统实验的补充两者相辅相成既能相互指导又能让您直观了解所做的变更如何影响系统在实际环境中的表现。What evals mean for business leadersEvery major technology shift reshapes operational excellence and competitive advantage. Frameworks like OKRs and KPIs have helped organizations orient themselves around “measuring what matters” for their business in the age of big data analytics. Evals are the natural extension of measurement for the age of AI.Working with probabilistic systems requires new kinds of measurement and deeper consideration of trade-offs. Leaders must decide when precision is essential, when they can be more flexible, and how to balance velocity and reliability.Evals are difficult to implement for the same reason that building great products is difficult; they require rigor, vision, and taste. If done well, evals become unique differentiators.In a world where information is freely available across the world and expertise is democratized, your advantage hinges on how well your systems can execute inside your context.Robust evals create compounding advantages and institutional know-how as your systems improve.At their core, evals are about a deep understanding of business context and objectives. If you cannot define what “great” means for your use case, you’re unlikely to achieve it. In this sense, evals highlight a key lesson of the AI era: management skills are AI skills. Clear goals, direct feedback, prudent judgment, and a clear understanding of your value proposition, strategy, and processes still matter, perhaps even more than ever.As more best practices and frameworks emerge, we will be sharing them. In the meantime, we encourage you to experiment with evals and discover what processes work best for your needs. To get started, identify the problem to be solved and your domain expert, round up your small team, and, if you are building on our API, explore our Platform Docs(opens in a new window).每一次重大的技术变革都会重塑卓越运营的标准与竞争优势的来源。在大数据分析时代OKR目标与关键结果和 KPI关键绩效指标等框架帮助企业确立了“衡量业务核心要素”的导向而在人工智能时代“评估”Evals则是这种衡量理念的自然延伸。与概率性系统打交道需要全新的衡量方式并对各种权衡取舍进行更深层次的考量。领导者必须做出决策何时必须追求精确何时可以保持灵活性以及如何在开发速度与系统可靠性之间取得平衡。实施评估体系之所以充满挑战原因与打造卓越产品如出一辙——这需要严谨的态度、宏大的愿景以及敏锐的判断力即“品味”。一旦实施得当评估体系将成为企业独特的竞争优势。当今世界信息触手可及专业知识日益普及企业的优势往往取决于系统在特定业务场景下的执行效能。随着系统的不断迭代优化完善的评估体系将带来复利效应般的优势积累并沉淀为企业的核心知识资产。归根结底评估的核心在于对业务背景与目标的深刻理解。如果你无法定义何为“卓越”的成效便很难真正实现它。从这个意义上说评估体系揭示了 AI 时代的一条重要启示管理能力即 AI 能力。清晰的目标、直接的反馈、审慎的判断以及对价值主张、战略和流程的透彻理解依然至关重要——甚至比以往任何时候都更为关键。我们将持续分享涌现出的各类最佳实践与框架。与此同时我们也鼓励您积极尝试各种评估方法探索最适合自身需求的流程。若想着手实践请先明确待解决的问题与相关领域的专家组建一支精干的小团队如果您基于我们的 API 进行开发欢迎查阅我们的平台文档Platform Docs。Don’t hope for “great.” Specify it, measure it, and improve toward it.--https://openai.com/index/evals-drive-next-chapter-of-ai/
How evals drive the next chapter in AI for businesses
Over one million businesses around the world are leveraging AI to drive greater efficiency and value creation. But some organizations have struggled to get the results they are expecting. What is causing the gap?At OpenAI we are leveraging AI internally to achieve our ambitious goals. One key set of tools we use areevals, methods to measure and improve the ability of an AI system to meet expectations.全球有超过一百万家企业正在利用人工智能来提升效率并创造更多价值。然而部分组织却难以获得预期的成效。造成这一差距的原因何在在 OpenAI我们也在内部利用人工智能来实现宏大的目标。我们使用的一套关键工具是“评估”evals——即用于衡量并提升人工智能系统满足预期能力的方法。Similar to product requirement documents, evals make fuzzy goals and abstract ideas specific and explicit. Using evals strategically can make a customer-facing product or internal tool more reliable at scale, decrease high-severity errors, protect against downside risk, and give an organization a measurable path to higher ROI.与产品需求文档类似评估evals能将模糊的目标和抽象的构想变得具体且明确。通过策略性地运用评估企业能够提升面向客户的产品或内部工具在大规模应用场景下的可靠性减少严重错误防范潜在风险并为实现更高的投资回报率ROI开辟一条可量化的路径。At OpenAI, our models are our products, so our researchers use rigorous frontier evals(opens in a new window) 1 to measure how well the models perform in different domains. While frontier evals help us ship better models faster, they cannot reveal all the nuances required to ensure the model will perform on a specific workflow in a specific business setting. That is why internal teams have also created dozens ofcontextual evalsdesigned to assess performance within a specific product or internal workflow. It is also why business leaders should learn how to create contextual evals specific to their organization’s needs and operating environment. 、在 OpenAI模型即产品因此我们的研究人员利用严谨的“前沿评估”frontier evals来衡量模型在不同领域的表现。尽管前沿评估有助于我们更高效地推出更优质的模型但它们无法揭示确保模型在特定业务场景及工作流程中表现良好所需的所有细节。正因如此内部团队开发了数十种“情境化评估”contextual evals专门用于评估模型在特定产品或内部工作流程中的表现。这也正是为什么业务领导者应当学习如何根据自身组织的需求与运营环境构建相应的“情境化评估”。This is a primer for business leaders looking to apply evals in their organizations. Contextual evals, each crafted for a specific organization’s workflow or product, are an active area of development and definitive processes have yet to emerge. As a result, this article provides a broad framework that we have seen work across many situations. We expect this field to evolve and for more frameworks to emerge that address specific business contexts and goals. For example, an excellent eval for a cutting-edge, AI-enabled consumer product might require a different process than an eval for an internal automation based around a standard operating procedure. We believe that the framework presented below will serve as a collection of best practices in both cases, and will be a useful guide as you build evals tailored to your organization’s needs.本文旨在为希望在组织内应用评估evals的商业领袖提供入门指南。“情境化评估”——即针对特定组织的工作流程或产品量身定制的评估——目前正处于积极发展阶段尚未形成标准化的流程。因此本文提供了一个在多种场景下均行之有效的通用框架。我们预计该领域将不断演进并涌现出更多针对特定业务背景和目标的评估框架。例如针对前沿AI赋能消费类产品的评估其流程可能与基于标准作业程序SOP的内部自动化系统的评估截然不同。我们相信下文介绍的框架汇集了适用于上述各类场景的最佳实践将为您根据组织需求构建评估体系提供有益的指导。How evals work: Specify → Measure → Improve1. Specify: Define what “great” meansStart with a small, empowered team that can write down the purpose of your AI system in plain terms, for example: “Convert qualified inbound emails into scheduled demos while staying on brand.”This team should be a mix of individuals with technical and domain expertise (in the given example, you’d want sales experts on the team). They should be able to state the most important outcomes to measure, outline the workflow end-to-end, and identify each important decision point your AI system will encounter. For every step in that workflow, the team should define what success looks like and what to avoid. This process will create a mapping of dozens of example inputs (e.g. inbound emails) to the outputs they want the system to produce. The resultinggolden setof examples should be a living, authoritative reference of your most skilled experts’ judgement and taste for what “great” looks like.首先组建一支精干且拥有自主权的小型团队让他们能用通俗易懂的语言阐明AI系统的目标——例如“将符合条件的入站邮件转化为已安排的演示预约同时保持品牌调性一致。”该团队应由具备技术专长和业务领域专长的人员共同组成以前述例子为例团队中应包含销售领域的专家。他们需要能够明确界定关键的衡量指标梳理端到端的业务流程并识别出AI系统将面临的每一个重要决策节点。针对流程中的每一个步骤团队都应明确“成功”的标准以及需要规避的问题。这一过程将建立起从各类输入样本如入站邮件到系统预期输出结果之间的对应关系。由此形成的“黄金样本集”golden set将成为一份动态更新的权威参考体现出你们最资深的专家对于何为“卓越”的判断标准与品味。Do not get overwhelmed with a cold start or try to solve it all at once. The process is iterative and messy. Early prototyping can help immensely. Reviewing 50 to 100 outputs from an early version of the system will uncover how and when your system is failing. This “error analysis” will result in a taxonomy of different errors (and their frequencies) to track as your system improves.This process is not purely technical—it’s cross-functional and centered on defining business goals and desired processes. Technical teams should not be asked in isolation to judge what best serves customers or the needs of other teams like product, sales, or HR. Consequently, domain experts, technical leads, and other key stakeholders should share ownership.面对“冷启动”阶段时切勿因无从下手而感到不知所措也不要试图毕其功于一役。这是一个充满反复与波折的迭代过程而早期的原型设计能提供极大的助益。通过审查系统早期版本生成的 50 到 100 个输出结果你可以发现系统在何种情况下以及何时出现故障。这种“错误分析”将帮助你建立一套错误分类体系并统计各类错误的发生频率以便在系统不断改进的过程中进行持续追踪。这一过程并非纯粹的技术工作而是一项跨职能协作其核心在于明确业务目标与理想流程。不应要求技术团队孤立地判断何种方案最能满足客户需求或是最符合产品、销售或人力资源等其他团队的要求。因此领域专家、技术负责人及其他关键利益相关方应当共同承担责任。2. Measure: Test against real-world conditionsThe next step is to measure. The goal of measurement is to reliably surface concrete examples of how and when the system is failing. To do that, create a dedicated test environment that closely mirrors real-world conditions—not just a demo or prompt playground. Evaluate performance against your golden set and error analysis under the same pressures and edge cases your system will actually face.Rubrics can help bring concreteness to judging outputs from your system, but it is possible to over-emphasize superficial items at the expense of your overall goals. Further, some qualities are difficult or impossible to measure. In some cases, traditional business metrics will be important. In others, you’ll need to invent new metrics. Keep your subject matter experts in the loop throughout, and tightly align the process with your core objectives.接下来是进行评估。评估的目标是可靠地找出系统在何种情况下、以何种方式发生故障的具体实例。为此你需要构建一个高度还原真实应用场景的专用测试环境而不仅仅是简单的演示环境或提示词prompt调试沙盒。请使用“黄金数据集”golden set来评估性能并在系统实际将要面临的压力负载和边界条件下进行错误分析。评估标准有助于量化系统输出的质量但若过度关注表层指标可能会导致偏离整体目标。此外某些质量特征难以甚至无法量化。在某些情况下传统的业务指标至关重要而在另一些情况下则需要制定全新的评估指标。在此过程中务必让领域专家全程参与并确保评估流程与核心目标保持高度一致。To actually test the system, use examples drawn from real-world situations whenever possible, and include or invent edge cases that are rare but costly if mishandled.Some evals can be scaled through the use of anLLM grader, an AI model that grades outputs the same way an expert would; yet, it is still important to keep a human in the loop. Your domain expert needs to regularly audit LLM graders for accuracy and should also directly review logs of your system’s behavior.Evals can help you decide when a system is ready to launch, but they do not stop at launch. You should continuously measure the quality of your systems real outputs generated from real inputs. As with any product, signals from your end-users (whether external or internal) are especially important and should be built into your eval.若要对系统进行实际测试应尽可能采用源自现实场景的案例并纳入或构想一些“边缘情况”——即那些虽不常见但若处理不当便会带来严重后果的特殊情形。部分评估工作可以借助“LLM 评分器”一种能像专家一样评估输出结果的 AI 模型来实现规模化不过让“人在回路”human-in-the-loop中保持参与依然至关重要。领域专家需要定期核查 LLM 评分器的准确性并直接审查系统运行行为的日志。评估不仅能帮助你判断系统何时可以上线其作用更不应止步于上线之时。你需要持续监测系统基于真实输入所产生的实际输出质量。正如对待任何产品一样来自终端用户无论是外部用户还是内部用户的反馈信号尤为重要应当将其纳入评估体系之中。3. Improve: Learn from errorsThe last step is to set up a process for continuous improvement. Addressing problems uncovered by your eval can take on many forms: refining prompts, adjusting data access, updating the eval itself to better reflect your goals, and so forth. As you uncover new types of errors, add them to your error analysis and address them. Each iteration compounds upon the last: new criteria and clearer expectations of system behavior help reveal new edge cases and subtle, stubborn issues to correct.To support this iteration, build a data flywheel. Log inputs, outputs, and outcomes; sample those logson a schedule and automatically route ambiguous or costly cases to expert review.Add these expert judgements to your eval and error analysis, then use them to update prompts, tools, or models. Through this loop you will more clearly define your expectations for the system, align it tighter to those expectations, and identify additional relevant outputs and outcomes to track. Deploying this process at scale yields a large, differentiated, context-specific dataset that is hard to copy—a valuable asset your organization can leverage as you build the best product or process in your market.While evals create a systematic way to improve your AI system, new failure modes can arise. In practice, as models, data, and business goals evolve, evals must also be continuously maintained, expanded, and stress-tested.For external-facing deployments, evals do not replace more traditional A/B tests and product experimentation. They are complements to traditional experimentation that can help guide each other and provide visibility into how changes you make impact real-world performance.最后一步是建立持续改进的流程。针对评估中发现的问题可以采取多种应对方式优化提示词prompts、调整数据访问权限、更新评估标准以更准确地反映目标等等。随着发现新型错误应将其纳入错误分析并加以解决。每一次迭代都建立在前一次的基础上新的评估标准和更明确的系统行为预期有助于揭示新的边界情况edge cases以及那些隐蔽且棘手的问题从而进行针对性修正。为了支持这种迭代应构建一个“数据飞轮”。记录输入、输出及最终结果定期对这些记录进行抽样并将模棱两可或处理成本高昂的案例自动分发给专家进行人工审核。将专家的判断结果整合到评估体系和错误分析中进而利用这些信息更新提示词、工具或模型。通过这一循环您能更清晰地定义对系统的期望使系统更紧密地契合这些期望并识别出更多值得追踪的相关输出与结果。大规模实施这一流程将产生一个庞大、独特且针对特定场景的数据集——这是一种难以被复制的宝贵资产能助力您的组织在市场上打造出卓越的产品或流程。尽管评估体系为改进AI系统提供了一种系统化方法但仍可能出现新的故障模式。在实际应用中随着模型、数据和业务目标的不断演变评估体系也必须持续进行维护、扩展和压力测试。对于面向外部的部署评估体系并不能取代传统的A/B测试和产品实验。它们是对传统实验的补充两者相辅相成既能相互指导又能让您直观了解所做的变更如何影响系统在实际环境中的表现。What evals mean for business leadersEvery major technology shift reshapes operational excellence and competitive advantage. Frameworks like OKRs and KPIs have helped organizations orient themselves around “measuring what matters” for their business in the age of big data analytics. Evals are the natural extension of measurement for the age of AI.Working with probabilistic systems requires new kinds of measurement and deeper consideration of trade-offs. Leaders must decide when precision is essential, when they can be more flexible, and how to balance velocity and reliability.Evals are difficult to implement for the same reason that building great products is difficult; they require rigor, vision, and taste. If done well, evals become unique differentiators.In a world where information is freely available across the world and expertise is democratized, your advantage hinges on how well your systems can execute inside your context.Robust evals create compounding advantages and institutional know-how as your systems improve.At their core, evals are about a deep understanding of business context and objectives. If you cannot define what “great” means for your use case, you’re unlikely to achieve it. In this sense, evals highlight a key lesson of the AI era: management skills are AI skills. Clear goals, direct feedback, prudent judgment, and a clear understanding of your value proposition, strategy, and processes still matter, perhaps even more than ever.As more best practices and frameworks emerge, we will be sharing them. In the meantime, we encourage you to experiment with evals and discover what processes work best for your needs. To get started, identify the problem to be solved and your domain expert, round up your small team, and, if you are building on our API, explore our Platform Docs(opens in a new window).每一次重大的技术变革都会重塑卓越运营的标准与竞争优势的来源。在大数据分析时代OKR目标与关键结果和 KPI关键绩效指标等框架帮助企业确立了“衡量业务核心要素”的导向而在人工智能时代“评估”Evals则是这种衡量理念的自然延伸。与概率性系统打交道需要全新的衡量方式并对各种权衡取舍进行更深层次的考量。领导者必须做出决策何时必须追求精确何时可以保持灵活性以及如何在开发速度与系统可靠性之间取得平衡。实施评估体系之所以充满挑战原因与打造卓越产品如出一辙——这需要严谨的态度、宏大的愿景以及敏锐的判断力即“品味”。一旦实施得当评估体系将成为企业独特的竞争优势。当今世界信息触手可及专业知识日益普及企业的优势往往取决于系统在特定业务场景下的执行效能。随着系统的不断迭代优化完善的评估体系将带来复利效应般的优势积累并沉淀为企业的核心知识资产。归根结底评估的核心在于对业务背景与目标的深刻理解。如果你无法定义何为“卓越”的成效便很难真正实现它。从这个意义上说评估体系揭示了 AI 时代的一条重要启示管理能力即 AI 能力。清晰的目标、直接的反馈、审慎的判断以及对价值主张、战略和流程的透彻理解依然至关重要——甚至比以往任何时候都更为关键。我们将持续分享涌现出的各类最佳实践与框架。与此同时我们也鼓励您积极尝试各种评估方法探索最适合自身需求的流程。若想着手实践请先明确待解决的问题与相关领域的专家组建一支精干的小团队如果您基于我们的 API 进行开发欢迎查阅我们的平台文档Platform Docs。Don’t hope for “great.” Specify it, measure it, and improve toward it.--https://openai.com/index/evals-drive-next-chapter-of-ai/
