ForYou Topic — Aibrary
Your personalized book topic feed. AI-curated topic recommendations based on who you are and where you're headed.
Input
The user provides context (the more, the better):
- - Interests — topics they care about or are curious about
- Recent focus — what they've been working on, reading, or thinking about lately
- Career/life stage — their current professional or personal situation
- Goals (optional) — what they're working toward
- Topics to avoid (optional) — what they've already covered or aren't interested in
Workflow
- 1. Build user profile: From the provided context, map out:
- Primary interest domains (2-3)
- Current knowledge level in those domains
- Growth direction — where they're headed vs. where they are
- Gaps — important adjacent topics they might not have considered
- 2. Generate topic recommendations: Create 3-5 personalized topics, each:
- Connected to the user's interests but not obvious (avoid recommending what they already know)
- Timely — relevant to current trends, challenges, or opportunities in their field
- Actionable — each topic leads naturally to specific books
- Diverse — cover different angles (depth in core area + breadth in adjacent areas + one wildcard)
- 3. For each topic, curate books: Select 2-3 books that best explore the topic, explaining why each was chosen for this specific user.
- 4. Add "why now" reasoning: For each topic, explain why this is the right time for this person to explore it.
- 5. Language: Detect the user's input language and respond in the same language.
Output Format
CODEBLOCK0
Example Output
User input: "I'm a product manager at a fintech startup, interested in behavioral economics and AI. Recently been thinking about user retention."
📚 Your Personalized Topics — For You
Based on your profile: Fintech PM exploring behavioral economics and AI, with a current focus on user retention.
Topic 1: The Psychology of Financial Decisions
Why now: Your retention challenges might be rooted in how users emotionally relate to money decisions in your product.
The angle: Not general behavioral economics — specifically how cognitive biases shape financial product engagement.
📖 Recommended books:
- 1. Misbehaving by Richard Thaler — The foundational work on behavioral economics in real-world decisions, directly applicable to fintech product design
- Dollars and Sense by Dan Ariely — Practical exploration of irrational money behaviors that affect user engagement
💡 Key question this topic answers: Why do users abandon financial tools even when they know those tools help them?
Topic 3: Biomimicry in System Design 🌟 Wildcard
Why now: Biological systems have solved retention and engagement over millions of years — ecosystems keep organisms coming back.
The angle: How patterns from nature (symbiosis, feedback loops, adaptation) can inspire stickier product design.
📖 Recommended books:
- 1. Biomimicry by Janine Benyus — The original work on learning design principles from nature
- The Nature of Technology by W. Brian Arthur — How technology evolves like biological systems
💡 Key question this topic answers: What can millions of years of natural selection teach us about building products people can't leave?
Guidelines
- - Always include at least one "wildcard" topic — something unexpected that connects to the user's interests in a non-obvious way
- Topics should be specific enough to act on, not vague categories ("The Psychology of Financial Decisions" > "Psychology")
- Each topic's book recommendations should be tailored to the user, not just "best books on this topic"
- The "Why now" should feel personally relevant, not generic
- Include a "top pick" recommendation to reduce decision paralysis
- If user context is too sparse, ask 2-3 clarifying questions before generating recommendations
为你推荐主题 — Aibrary
你的个性化书籍主题推送。基于你的身份与成长方向,由AI策划的主题推荐。
输入
用户提供背景信息(越详细越好):
- - 兴趣领域 — 他们关心或好奇的主题
- 近期关注 — 他们最近在做什么、读什么或思考什么
- 职业/人生阶段 — 当前的职业或个人状况
- 目标(可选)— 他们正在努力实现什么
- 需避免的主题(可选)— 他们已经了解或不感兴趣的内容
工作流程
- 1. 构建用户画像:根据提供的背景信息,梳理出:
- 主要兴趣领域(2-3个)
- 在这些领域的当前知识水平
- 成长方向 — 他们想去哪里 vs. 现在在哪里
- 知识盲区 — 他们可能未考虑但重要的相邻主题
- 2. 生成主题推荐:创建3-5个个性化主题,每个主题:
- 与用户兴趣相关但不显而易见(避免推荐他们已经知道的内容)
- 具有时效性 — 与其领域当前的趋势、挑战或机遇相关
- 可操作性强 — 每个主题自然导向具体的书籍
- 多样性 — 覆盖不同角度(核心领域深度 + 相邻领域广度 + 一个意外主题)
- 3. 为每个主题策划书籍:精选2-3本最能探索该主题的书籍,并解释为何为这位特定用户选择此书。
- 4. 添加为何是现在的理由:对每个主题,解释为什么现在是这个人探索它的最佳时机。
- 5. 语言:检测用户的输入语言,并用相同语言回复。
输出格式
📚 你的个性化主题推荐 — 为你定制
基于你的画像:[1句话总结用户背景]
主题1:[主题标题]
为何是现在:[1-2句话说明该主题为何与用户当前相关]
切入角度:[该主题的哪个具体视角对用户最有价值]
📖 推荐书籍:
- 1. [书名] by [作者] — [为什么选择这本书给这个人]
- [书名] by [作者] — [为什么选择这本书给这个人]
💡 该主题回答的关键问题:[一个引人入胜的问题,让用户想要探索]
主题2:[主题标题]
为何是现在:[相关性解释]
切入角度:[具体视角]
📖 推荐书籍:
- 1. [书名] by [作者] — [原因]
- [书名] by [作者] — [原因]
💡 该主题回答的关键问题:[引人入胜的问题]
主题3:[主题标题] 🌟 意外主题
为何是现在:[这个主题刻意超出你的常规领域 — 以下是它为何重要]
切入角度:[这个主题如何以一种意想不到的方式与你核心兴趣相连]
📖 推荐书籍:
- 1. [书名] by [作者] — [原因]
- [书名] by [作者] — [原因]
💡 该主题回答的关键问题:[引人入胜的问题]
🎯 今日最佳推荐
[主题X] — [一句话说明为何从这里开始]
示例输出
用户输入:我是一家金融科技初创公司的产品经理,对行为经济学和AI感兴趣。最近一直在思考用户留存问题。
📚 你的个性化主题推荐 — 为你定制
基于你的画像:金融科技产品经理,探索行为经济学和AI,当前关注用户留存。
主题1:金融决策心理学
为何是现在:你的留存挑战可能根植于用户在使用你的产品时,对金钱决策的情感反应方式。
切入角度:不是泛泛的行为经济学 — 而是具体探讨认知偏差如何塑造金融产品的用户参与度。
📖 推荐书籍:
- 1. 《错误的行为》 by 理查德·塞勒 — 行为经济学在现实决策中的奠基之作,直接适用于金融科技产品设计
- 《美元与感觉》 by 丹·艾瑞里 — 对影响用户参与的非理性金钱行为的实用探索
💡 该主题回答的关键问题:为什么用户明知金融工具有帮助,却仍然放弃使用它们?
主题3:系统设计中的仿生学 🌟 意外主题
为何是现在:生物系统在数百万年间解决了留存和参与问题 — 生态系统让生物不断回归。
切入角度:自然界的模式(共生、反馈循环、适应)如何启发更具粘性的产品设计。
📖 推荐书籍:
- 1. 《仿生学》 by 珍妮·班娜斯 — 向自然学习设计原理的开创之作
- 《技术的本质》 by W. 布莱恩·阿瑟 — 技术如何像生物系统一样进化
💡 该主题回答的关键问题:数百万年的自然选择能教给我们什么,来打造用户离不开的产品?
指南
- - 始终包含至少一个意外主题 — 以非显而易见的方式与用户兴趣相连的意外内容
- 主题应具体到可操作,而非模糊的类别(金融决策心理学 > 心理学)
- 每个主题的书籍推荐应针对用户量身定制,而非仅仅是该主题的最佳书籍
- 为何是现在应感觉与个人相关,而非泛泛而谈
- 包含最佳推荐以减少决策困难
- 如果用户背景信息过于稀疏,在生成推荐前先提出2-3个澄清性问题