独立搜索产品2024 · 月访问量 Monthly visits
独立首页 · 过程展示 · 来源追溯
- 360360 AI 搜索287M
- PPerplexity94M
- 秘塔 AI 搜索7M
- 天天工 AI 搜索5M
我负责的主产品是 AI 搜索。
如今,AI 搜索已经成为原子化能力,内化到了模型、Harness 等部分。
而当时的策略,都来源于一个具体的时代背景:2024 Q3,AI 搜索正在寻找自己的形态。
My primary product was AI Search. Today, search is an atomic capability embedded across models, harnesses, and related systems. In Q3 2024, AI search was still discovering its product form.
01 调研 Research
各家 AI 搜索在同一时期密集崛起。独立搜索产品、对话产品、传统搜索厂商都在寻找入口、过程和结果的最优形态;用户也在第一次形成“AI 可以替我搜索”的认知。
Search-first products, chat products, and incumbent search companies were simultaneously exploring the best form for entry, process, and results.
独立首页 · 过程展示 · 来源追溯
主对话入口 · 搜索调用 · 对话内呈现
独立入口会产生独立预期。只要入口叫“AI 搜索”,用户就会对搜索产生预期。反过来,为了强化用户的“搜索”心智,产品本身也应该与 Chatbot 形态区分开。
A dedicated search entry creates clear expectations. Strengthening that search mindset also requires a product form distinct from a chatbot.02 设计策略 Product strategy
接手时,产品仅具备 LLM + Web Search 能力,但产品形态仍延续 Chatbot 形态,没有拉开差异。改版沿着三件事展开——打开即搜索、运行中看见检索、搜索结果可溯源。
The product had LLM and web-search capabilities, while its experience still followed the chatbot form. The redesign focused on entry, visible retrieval, and traceable results.
The homepage establishes category recognition before the first query: a focused search box, clear identity, and lightweight prompts.
Query decomposition, source reading, and progress feedback turn waiting time into visible product work.
Trust becomes a product interaction when users can inspect source titles, summaries, and original pages.
搜索过程 · 16 秒 · 默认 2× · 用户身份与会话侧栏已裁切16 sec · default 2× · personal identity and chat history removed
这些设计在今天已经非常常见。回到 2024 Q4,它们属于第一批定义 AI 搜索产品形态的尝试;随后,过程可见、来源可核验与搜索侧栏逐渐成为 AI Native 产品的基础部件。Common today, these patterns were still being defined in late 2024. They later became foundational parts of AI-native products.
03 新功能 Experiment
理念上,它希望 AI 搜索从“搜答案”继续走向“刷知识”,为一轮回答生成可继续探索的知识结构。
The feature extended answer retrieval into a stream of knowledge that users could continue exploring.
客观问答触发旁路模型,把用户问题扩展成三组知识点,点击后继续搜索。A side model expanded objective questions into three navigable groups of knowledge.
产品的搜索心智仍在建立,用户关注点还停留在“准不准”,对于创新形态的接受度可能不高。The search mindset was still forming, and users remained focused on answer accuracy, which could limit adoption of a new interaction form.
上线后的行为数据验证了上线前的担心。当时搜索的核心还是搜得准,用户更关注回答以及来源。Post-launch behavior confirmed the concern: users cared most about accurate answers and their sources.
两项数据来自不同功能埋点,用于观察用户选择方向。Separate feature metrics, compared here to show the direction of user attention.
个人观点:当时做的知识卡片略显急促,只是把对话底部的推荐问题改变了形式,真正的“猜你喜欢”还需要用户画像、推荐系统与搜索上下文共同参与。Personal view: the knowledge-card release moved too quickly. It mainly changed the presentation of follow-up prompts; true “for you” recommendations need user profiles, ranking systems, and search context.
04 产品规划 Planning
2025 年 1 月 14 日,参与了 AI 搜索 2025 年度规划。根据上线数据、用户反馈、搜索技术链路,我把后续规划收敛为两个方向:可信 AI 与搜索原子化。
On January 14, 2025, I contributed to the annual AI-search plan and organized the next stage around trustworthy AI and atomic search.
规划依据:来源侧栏点击率达到 60%,用户会主动核验回答依据。
规划动作:识别权威信源,通过多源交叉验证降低偏差,让引用持续可见、可以追溯,并处理信息冲突与内容时效。
Prioritize authoritative sources, cross-source checks, traceable citations, and recency handling.规划依据:AI 搜索是 LLM 信息的补充,该能力将会应用在不同的产品中,用户直接进入独立搜索产品只是当下的中间态。
规划动作:支撑清言主 Chat 的搜索调用,形成 API 与浏览器插件能力,并接入第三方产品、Agent 和 Workflow。
AI search supplements the information available to an LLM and can support many products; a standalone search destination was an intermediate product form.产品最核心是要解决用户需求。当底层模型能力趋同时,Context 决定了模型表现,此时搜得准不准决定了产品是否能准确理解用户需求;因此,AI 搜索也从独立页面进入更底层的模型能力中。
Products begin with user needs. As foundation models converge, context shapes model performance, and retrieval quality determines how accurately a product understands those needs. AI search has therefore moved from a standalone page into deeper model capabilities.05 其他参与 Also shipped
三项工作均已上线,这里保留简要记录。
Three additional product contributions shipped during the same period.
让“附近美食”等查询获得可用的位置上下文;参与方案并推进上线。Added usable location context for nearby recommendations and supported delivery.
围绕导游与教师场景完成两轮用户研究、形象方向判断,并参与后续上线。Ran two rounds of research for guide and teacher scenarios, shaped the avatar direction, and supported launch.
梳理多端生成与滚动体验,推动交互规则统一并上线。Aligned generation and scrolling behavior across clients and brought the unified rules to launch.