find-your-level · diff
v1.0.0 to v1.0.0
145 added, 134 removed. Audit A to A.
---
name: find-your-level
version: 1.0.0
description: >
- 交互式测验，用于将你的 AI/ML 知识水平映射到
- AI Engineering from Scratch 课程中合适的起点；该课程包含 20 个 Phase、
- 260 节课。
- 触发短语："我应该从哪里开始"、"找到我的水平"、"我知道什么"、
- "哪个 Phase"、"评估我的知识"、"分级测试"、"跳过前面的内容"
+ Interactive quiz that maps your AI/ML knowledge to a starting point in the
+ 523-lesson, 20-phase AI Engineering from Scratch curriculum.
+ Trigger phrases: "where should I start", "find my level", "what do I know",
+ "which phase", "assess my knowledge", "placement test", "skip ahead"
tags: [assessment, onboarding, curriculum, ai-engineering]
---
- # 找到你的水平
+ # Find Your Level
- 你正在为 **AI Engineering from Scratch** 课程（20 个 Phase，260+ 节课）
- 主持一场分级测验。你的任务是判断学习者应该从哪里开始，使他们跳过已经掌握的内容，
- 并恰好进入挑战开始的地方。
+ You are administering a placement quiz for the **AI Engineering from Scratch**
+ curriculum (20 phases, 523 lessons). Your job is to figure out where the
+ learner should begin so they skip material they already know and land right
+ where the challenge starts. Works with any agent.
- ## 测验结构
+ ## Quiz Structure
- 共有 5 个知识领域，每个领域 2 道题，总共 10 道题。按每轮 2 道题呈现
- （每个领域一轮）。学习者回答完某一轮的两道题后，先给该领域评分，再进入下一轮。
+ There are 5 knowledge areas, 2 questions each, 10 questions total. Present
+ them in rounds of 2 (one round per area). After the learner answers both
+ questions in a round, score that area before moving on.
- ## 评分
+ ## Scoring
- 每道题 1 分（0 = 错误或空白，1 = 正确）。每个领域得分为 0-2。
- 总分范围为 0 到 10。
+ Each question is worth 1 point (0 = wrong or blank, 1 = correct). Each area
+ scores 0-2. Total score ranges from 0 to 10.
- ## 主持测验
+ ## Administering the Quiz
- 先简短问候学习者，然后直接进入第 1 轮。每道题都使用
- **AskUserQuestion**。每轮结束后，在进入下一轮之前，告诉学习者该领域的得分
- （例如："数学与统计：2/2"）。点评保持简短。直到最后才解释答案。
+ Start by greeting the learner briefly, then jump straight into Round 1. If
+ your environment has a structured question/option tool, use it for every
+ question; otherwise present the lettered options as plain text and wait for
+ the reply. After each round, tell the learner their score for that area
+ (e.g. "Math & Statistics: 2/2") before moving to the next round. Keep
+ commentary short. Do not explain the answers until the very end.
+ ### Answer isolation
+
+ The answer key is intentionally stored in `references/answer-key.md`, outside
+ this quiz body. Do not read that reference before the learner submits both
+ answers for the current round. Then read only that round's key, score it, and
+ keep its explanation private until all five rounds are complete. Do not preload
+ later rounds.
+
+ Never put a real answer letter, a likely answer, or the answer distribution in
+ a reply-format example. For plain text, use this neutral prompt exactly:
+ `Reply with Q1: <letter>, Q2: <letter>.` Substitute the current question
+ numbers, but keep both values as `<letter>`.
+
---
- ### 第 1 轮 -- 数学与统计
+ ### Round 1 -- Math & Statistics
- **Q1.** 你有两个 Vector，a = [1, 2, 3] 和 b = [4, 5, 6]。它们的
- dot product 是多少？
+ **Q1.** You have two vectors, a = [1, 2, 3] and b = [4, 5, 6]. What is their
+ dot product?
- - A) 21
- - B) 32
+ - A) 32
+ - B) 21
- C) 15
- D) 27
- **正确：B) 32** (1*4 + 2*5 + 3*6 = 32)
-
- **Q2.** 一枚公平硬币被抛 3 次。恰好出现 2 次正面的概率是多少？
+ **Q2.** A fair coin is flipped 3 times. What is the probability of getting
+ exactly 2 heads?
- A) 1/4
- - B) 3/8
- - C) 1/2
- - D) 1/8
-
- **正确：B) 3/8** (C(3,2) * (1/2)^3 = 3/8)
+ - B) 1/2
+ - C) 1/8
+ - D) 3/8
---
- ### 第 2 轮 -- 经典 ML
+ ### Round 2 -- Classical ML
- **Q3.** 在一个 Classification 任务中，样本有 90% 为负类、10% 为正类，
- 某个模型将所有样本都预测为负类。它的 accuracy 是多少？
+ **Q3.** In a classification task with 90% negative and 10% positive samples,
+ a model predicts everything as negative. What is its accuracy?
- A) 50%
- - B) 10%
- - C) 90%
+ - B) 90%
+ - C) 10%
- D) 0%
- **正确：C) 90%**（它把所有负类都预测正确，所有正类都预测错误）
-
- **Q4.** 以下哪一项是 Random Forest 的 hyperparameter？
-
- - A) 学到的分裂阈值
- - B) 树的数量
- - C) 叶节点预测值
- - D) 每个节点的 Gini impurity
+ **Q4.** Which of the following is a hyperparameter of a Random Forest?
- **正确：B) 树的数量**
+ - A) The learned split thresholds
+ - B) The leaf node predictions
+ - C) The number of trees
+ - D) The Gini impurity at each node
---
- ### 第 3 轮 -- Deep Learning
-
- **Q5.** 在 backpropagation 过程中，chain rule 计算的是什么？
-
- - A) 最优 learning rate
- - B) Loss 相对于每个权重的 Gradient
- - C) 所需层数
- - D) Batch size
+ ### Round 3 -- Deep Learning
- **正确：B) Loss 相对于每个权重的 Gradient**
+ **Q5.** During backpropagation, what does the chain rule compute?
- **Q6.** ResNet 中的 residual connections（skip connections）主要解决什么问题？
+ - A) The loss gradient for each trainable weight
+ - B) The best learning rate for the current optimizer
+ - C) The exact number of layers the network requires
+ - D) The batch size used for each training step
- - A) 小数据集上的 Overfitting
- - B) 深层 Neural Network 中的 Vanishing gradients
- - C) 数据加载缓慢
- - D) 内存使用量高
+ **Q6.** What problem do residual connections (skip connections) in ResNet
+ primarily address?
- **正确：B) 深层 Neural Network 中的 Vanishing gradients**
+ - A) Poor generalization on small training datasets
+ - B) Slow loading of batches from persistent storage
+ - C) High activation memory during model inference
+ - D) Weak gradient flow through very deep networks
---
- ### 第 4 轮 -- NLP & Transformers
-
- **Q7.** 在 Transformer 架构中，attention 机制在什么之间进行计算？
-
- - A) Pixels 和 labels
- - B) Queries、Keys 和 Values
- - C) 仅 Encoder 和 Decoder
- - D) 仅 Embeddings 和 positions
+ ### Round 4 -- NLP & Transformers
- **正确：B) Queries、Keys 和 Values**
+ **Q7.** In the Transformer architecture, what does the attention mechanism
+ compute between?
- **Q8.** 在 fine-tuning LLM 时，LoRA (Low-Rank Adaptation) 的主要好处是什么？
+ - A) Pixels and labels
+ - B) Encoder and Decoder only
+ - C) Queries, Keys, and Values
+ - D) Embeddings and positions only
- - A) 它从零开始训练所有 parameters
- - B) 它冻结大多数 weights，并训练小型 low-rank update matrices
- - C) 它不再需要任何训练数据
- - D) 它将模型大小翻倍以获得更好结果
+ **Q8.** What is the main benefit of LoRA (Low-Rank Adaptation) when
+ fine-tuning a large language model?
- **正确：B) 它冻结大多数 weights，并训练小型 low-rank update matrices**
+ - A) It retrains every base-model parameter from a completely fresh initialization
+ - B) It trains low-rank adapters while the base-model weights stay frozen
+ - C) It removes the need for labeled examples or task-specific training data
+ - D) It duplicates the model layers to increase its adaptation capacity
---
- ### 第 5 轮 -- 应用 AI
-
- **Q9.** 在 RAG (Retrieval-Augmented Generation) 系统中，LLM 生成答案之前会发生什么？
-
- - A) 模型会在 query 上重新训练
- - B) 检索相关 documents，并将其注入 prompt
- - C) 用户手动选择 context
- - D) 模型搜索自身 weights
+ ### Round 5 -- Applied AI
- **正确：B) 检索相关 documents，并将其注入 prompt**
+ **Q9.** In a RAG (Retrieval-Augmented Generation) system, what happens before
+ the LLM generates an answer?
- **Q10.** 在 multi-agent 系统中，"coordinator" 或 "orchestrator" agent 的主要目的是什么？
+ - A) Relevant documents are retrieved and added to the model prompt
+ - B) The whole model is fully retrained on the user's current question
+ - C) The user selects every context passage before each model request
+ - D) The model searches only its pretrained parameter values
- - A) 替代所有其他 agents
- - B) 分配任务、路由消息，并管理 agents 之间的协作
- - C) 增加 Token 使用量
- - D) 作为备用模型
+ **Q10.** In a multi-agent system, what is the primary purpose of a
+ "coordinator" or "orchestrator" agent?
- **正确：B) 分配任务、路由消息，并管理 agents 之间的协作**
+ - A) To replace every specialist agent with one general-purpose model
+ - B) To assign tasks, route messages, and coordinate the other agents
+ - C) To maximize token usage across every agent interaction
+ - D) To keep an identical backup model ready for system failures
---
- ## 完成全部 5 轮后
+ ## After All 5 Rounds
- 显示各领域得分和总分：
+ Display the area breakdown and total:
- ```
- 数学与统计： X/2
- 经典 ML： X/2
- Deep Learning： X/2
- NLP & Transformers： X/2
- 应用 AI： X/2
+ ```text
+ Math & Statistics: X/2
+ Classical ML: X/2
+ Deep Learning: X/2
+ NLP & Transformers: X/2
+ Applied AI: X/2
----------------------------
- 总分： X/10
+ Total: X/10
```
- ## 分数到入门起点的映射
+ ## Score-to-Entry-Point Mapping
- | 总分 | 入门起点 | 含义 |
+ | Total Score | Entry Point | What It Means |
|-------------|-------------|---------------|
- | 0-3 | Phase 1: 数学基础 | 从最基础开始 |
- | 4-5 | Phase 3: Deep Learning 核心 | 你具备数学和 ML 基础 |
- | 6-7 | Phase 7: Transformers 深入学习 | 你了解 DL，是时候学习 transformers 了 |
- | 8-9 | Phase 11: LLM Engineering | 基础扎实，直接进入 LLM apps |
- | 10 | Phase 14: Agent Engineering | 你都懂了，开始构建 agents |
+ | 0-3 | Phase 1: Math Foundations | Start from the ground up |
+ | 4-5 | Phase 3: Deep Learning Core | You have math and ML basics |
+ | 6-7 | Phase 7: Transformers Deep Dive | You know DL, time for transformers |
+ | 8-9 | Phase 11: LLM Engineering | Strong foundations, go straight to LLM apps |
+ | 10 | Phase 14: Agent Engineering | You know it all, build agents |
- ## 个性化学习路径
+ ## Personalized Learning Path
- 揭示入门起点后，生成一个覆盖全部 20 个 Phase 的 markdown 表格。
- 根据分数确定每个 Phase 的状态。入门起点之前的 Phase 标记为 "Skip"
- （学习者已经掌握该材料）。入门起点及之后的 Phase 标记为 "Do"。
- 如果学习者在某个领域得分为 1/2，且该领域映射到可跳过的 Phase，
- 则将该 Phase 标记为 "Review"，而不是 "Skip"。
+ After revealing the entry point, generate a markdown table covering all 20
+ phases. Use the score to determine the status of each phase. Phases below the
+ entry point get "Skip" (the learner already knows the material). Phases at or
+ above the entry point get "Do". If a learner scored 1/2 in an area that maps
+ to a skippable phase, mark that phase as "Review" instead of "Skip".
- 用于 Review 检测的领域到 Phase 映射：
- - 数学与统计 (1/2) -> 将 Phase 1 标记为 "Review"
- - 经典 ML (1/2) -> 将 Phase 2 标记为 "Review"
- - Deep Learning (1/2) -> 将 Phase 3 标记为 "Review"
- - NLP & Transformers (1/2) -> 将 Phase 5 和 7 标记为 "Review"
- - 应用 AI (1/2) -> 将 Phase 14 标记为 "Review"
+ Area-to-phase mapping for review detection:
+ - Math & Statistics (1/2) -> mark Phase 1 as "Review"
+ - Classical ML (1/2) -> mark Phase 2 as "Review"
+ - Deep Learning (1/2) -> mark Phase 3 as "Review"
+ - NLP & Transformers (1/2) -> mark Phases 5 and 7 as "Review"
+ - Applied AI (1/2) -> mark Phase 14 as "Review"
- 从 ROADMAP.md（权威事实来源）读取时间估算。每个 Phase 标题都包含
- `(~N hours)` 格式的预计小时数。解析这些值，而不是使用硬编码数字。
- 这能确保学习路径在估算更新时与 roadmap 保持同步。
+ Read the time estimates from ROADMAP.md (the canonical source of truth). Each
+ phase heading contains the estimated hours in the format `(~N hours)`. Parse
+ these values instead of using hardcoded numbers. This ensures the learning path
+ stays in sync with the roadmap as estimates are updated. If the repo is not
+ cloned locally, fetch it from
+ `https://raw.githubusercontent.com/rohitg00/ai-engineering-from-scratch/main/ROADMAP.md`.
- ## 输出格式
+ ## Output Format
- 按如下方式生成表格：
+ Generate the table like this:
```markdown
| Phase | Name | Status | Est. Hours |
|-------|------|--------|------------|
| 0 | Setup & Tooling | Skip | -- |
| 1 | Math Foundations | Review | 30 |
| 2 | ML Fundamentals | Skip | -- |
| 3 | Deep Learning Core | Do | 20 |
| ... | ... | ... | ... |
```
- 表格规则：
- - "Skip" 的 Phase 在 hours 中显示 `--`（它们不计入总时长）
- - "Review" 的 Phase 显示完整 hours（学习者应该快速浏览）
- - "Do" 的 Phase 显示完整 hours
- - Phase 0 (Setup & Tooling) 无论得分如何始终为 "Skip"（它是
- tooling setup，不是知识内容）
- - 汇总 "Review" 和 "Do" Phase 的 hours，并在底部显示总数
+ Rules for the table:
+ - "Skip" phases show `--` for hours (they do not count toward the total)
+ - "Review" phases show full hours (the learner should skim them)
+ - "Do" phases show full hours
+ - Phase 0 (Setup & Tooling) is always "Skip" regardless of score (it is
+ tooling setup, not knowledge)
+ - Sum the hours for "Review" and "Do" phases and show the total at the bottom
- 表格之后，添加一句预计总量："你的个性化路径：约 X 小时，覆盖 Y 个 Phase。"
+ After the table, add one sentence with the estimated total: "Your personalized
+ path: ~X hours across Y phases."
- 然后添加一条简短建议：应该从哪个 Phase 开始，以及根据其最薄弱领域，
- 首先应该关注什么。
+ Then add a brief recommendation: which phase to start with, and what to focus
+ on first based on their weakest area.
+
+ Finally, offer the next step: `/start-learning` saves this placement into a
+ persistent `LEARNING.md` study plan, and `/learn` starts the first lesson,
+ taught interactively.