ai-product-strategy

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AI Product Strategy

AI产品策略

Help the user make strategic decisions about AI products using frameworks from 94 product leaders and AI practitioners.

借助94位产品负责人和AI从业者总结的框架，帮助用户制定AI产品相关的战略决策。

How to Help

如何提供帮助

When the user asks for help with AI product strategy:

Understand the context - Ask what they're building, what problem they're solving, and where they are in the AI journey
Clarify the problem - Help distinguish between "AI for AI's sake" and genuine user problems that AI can solve
Guide architecture decisions - Help them think through build vs buy, model selection, and human-AI boundaries
Plan for iteration - Emphasize feedback loops, evals, and building for rapid model improvements

当用户寻求AI产品策略相关帮助时：

了解背景 - 询问他们正在构建什么、要解决什么问题，以及处于AI落地旅程的哪个阶段
明确问题 - 帮助区分“为了AI而AI”和AI能真正解决的用户痛点
指导架构决策 - 协助他们梳理自建vs采购、模型选择以及人机协作边界的相关思路
规划迭代方案 - 强调反馈循环、评估（evals）以及为模型快速迭代优化而构建系统的重要性

Core Principles

核心原则

Start with the problem, not the AI

从问题出发，而非从AI出发

Aishwarya Naresh Reganti: "In all the advancements of AI, one slippery slope is to keep thinking about solution complexity and forget the problem you're trying to solve. Start with minimal impact use cases to gain a grip on current capabilities."

Aishwarya Naresh Reganti：“在AI的所有技术进展中，一个容易陷入的误区是一味关注解决方案的复杂性，却忘记了要解决的核心问题。从影响最小的用例入手，逐步掌握当前AI的能力边界。”

Define the human-AI boundary

明确人机协作边界

Adriel Frederick: "When working on algorithmic products, your job is figuring out what the algorithm should be responsible for, what people are responsible for, and the framework for making decisions." This boundary is the core PM decision.

Adriel Frederick：“在开发算法驱动的产品时，你的核心工作是明确算法负责什么、人类负责什么，以及制定决策的框架。”这个边界是产品经理（PM）的核心决策点。

AI is magical duct tape

AI是神奇的“万能胶带”

Alex Komoroske: "LLMs are magical duct tape—distilled intuition of society. They make writing 'good enough' software significantly cheaper but increase marginal inference costs." Understand the new cost structure.

Alex Komoroske：“LLMs是神奇的‘万能胶带’——凝聚了社会的集体直觉。它们能大幅降低写出‘够用’软件的成本，但会增加边际推理成本。”要理解这种新的成本结构。

Build for the slope, not the snapshot

为长期趋势构建，而非当下状态

Asha Sharma: "You have to build for the slope instead of the snapshot of where you are." AI capabilities change fast—build flexible architectures that can swap models as they improve.

Asha Sharma：“你必须着眼于长期发展趋势来构建系统，而非只看当前的状态。”AI能力迭代速度极快——要构建灵活的架构，以便随着模型升级可以轻松替换。

Design for squishiness

为“不确定性”设计

Alex Komoroske: "Even at 99% accuracy, if it punches the user in the face 1% of the time, that's not a viable product. Design assuming the AI will be squishy and not fully accurate."

Alex Komoroske：“即使准确率达到99%，如果有1%的概率给用户带来糟糕体验，那也不是一个可行的产品。要假设AI存在不确定性、并非完全准确来进行设计。”

Flywheels beat first-mover advantage

飞轮效应胜过先发优势

Aishwarya Naresh Reganti: "It's not about being first to have an agent. It's about building the right flywheels to improve over time." Log human actions to create data loops for system improvement.

Aishwarya Naresh Reganti：“关键不在于率先推出Agent，而在于构建正确的飞轮效应来实现持续改进。”记录人类操作行为，创建数据循环以推动系统迭代优化。

Society of models, not single models

多模型协同，而非单一模型

Amjad Masad: "Future products will be made of many different models—it's quite a heavy engineering project." Use specialized models for different tasks (reasoning vs speed vs coding).

Amjad Masad：“未来的产品将由多个不同模型组成——这是一项复杂度很高的工程任务。”针对不同任务使用专用模型（比如推理、速度、编码等场景）。

Use the right tool for each task

为不同任务选择合适工具

Albert Cheng: "We run chess engines for evaluations. LLMs translate that into natural language. Use the right technology for the right task." Don't use LLMs where deterministic algorithms excel.

Albert Cheng：“我们用国际象棋引擎进行评估，再用LLMs将结果转化为自然语言。要为不同任务选择合适的技术。”不要在确定性算法更擅长的场景中使用LLMs。

Humans are the bottleneck

人类是当前的瓶颈

Alexander Embiricos: "The current limiting factor is human typing speed and multitasking on prompts. Build systems that are 'default useful' without constant prompting."

Alexander Embiricos：“当前的限制因素是人类的打字速度和多任务处理提示词的能力。要构建无需频繁输入提示词就能‘默认有用’的系统。”

Account for non-determinism

考虑非确定性

Aishwarya Naresh Reganti: "Most people ignore the non-determinism. You don't know how users will behave with natural language, and you don't know how the LLM will respond." Build for variability.

Aishwarya Naresh Reganti：“大多数人忽略了非确定性问题。你无法预知用户会如何使用自然语言交互，也无法预知LLM会给出怎样的回应。”要为这种可变性进行系统构建。

Agents need autonomy + complexity + natural interaction

Agent需要自主性+复杂性+自然交互

Aparna Chennapragada: "Effective agents have (1) increasing autonomy to handle higher-order tasks, (2) ability to handle complex multi-step workflows, and (3) natural, often asynchronous interaction."

Aparna Chennapragada：“有效的Agent具备三个特征：(1) 处理高阶任务的自主性不断提升；(2) 处理复杂多步骤工作流的能力；(3) 自然且通常是异步的交互方式。”

Rebuild your intuitions

重构你的直觉认知

Aishwarya Naresh Reganti: "Leaders have to get hands-on—not implementing, but rebuilding intuitions. Be comfortable that your intuitions might not be right." Block time daily to stay current.

Aishwarya Naresh Reganti：“领导者必须亲身体验——不是亲自实现，而是重构对AI的直觉认知。要接受自己原有的直觉可能不再适用。”每天预留时间跟进AI领域的最新动态。

Questions to Help Users

用于引导用户的问题

"What specific user problem are you solving with AI?"
"What should the AI decide vs. what should humans decide?"
"How will you handle the 5% of cases where the AI fails?"
"What feedback loops will improve the system over time?"
"Are you building for today's model capabilities or anticipating improvements?"
"Have you set up evals and observability?"

“你想用AI解决什么具体的用户痛点？”
“哪些决策应该由AI做出，哪些应该由人类做出？”
“你将如何处理AI出错的那5%场景？”
“你将建立哪些反馈循环来持续优化系统？”
“你是基于当前模型能力构建系统，还是在为未来的模型升级做准备？”
“你是否已经建立了评估（evals）和可观测性机制？”

Common Mistakes to Flag

需要指出的常见误区

AI for AI's sake - Adding AI features without clear user problems
Single-model thinking - Not considering specialized models for different tasks
Ignoring the failures - Not designing UX for when AI gets it wrong
Static architecture - Building systems that can't evolve with model improvements
Skipping evals - Not establishing measurement and observability from day one
Over-automation - Removing humans from loops where they add value

为了AI而AI - 在没有明确用户痛点的情况下添加AI功能
单一思维模型 - 未考虑针对不同任务使用专用模型
忽视故障场景 - 未针对AI出错的情况设计用户体验（UX）
静态架构 - 构建的系统无法随模型升级而演进
跳过评估环节 - 从项目第一天起就未建立度量和可观测性机制
过度自动化 - 在人类能创造价值的环节移除人工参与

Deep Dive

深入探索

For all 179 insights from 94 guests, see

references/guest-insights.md

如需查看来自94位嘉宾的全部179条见解，请参阅

references/guest-insights.md