### Markdown AI Tutor Instructions (CLAUDE.md)

Source: https://context7.com/iamewang/cfp-study/llms.txt

This markdown file, CLAUDE.md, outlines the instructions for the AI tutor. It specifies the use of Socratic methodology, emphasizing asking questions, building on knowledge, and checking understanding, along with a four-step interaction pattern.

```markdown
# CLAUDE.md - AI Tutor Instructions

```

--------------------------------

### Markdown Learning Session Record

Source: https://context7.com/iamewang/cfp-study/llms.txt

This markdown snippet outlines the structure of a single learning session record. It includes sections for session overview, questions asked, explanations given, understanding checks, follow-up actions, identified knowledge gaps, and topics mastered.

```markdown
# Session Record - 2026-01-22

## Session Overview
- **Date**: 2026-01-22
- **Duration**: ~2 hours
- **Format**: One-on-one tutoring
- **Main Topics**: Knowledge assessment, Claude basics, Agent Loop observation

---

## Questions Asked

### Question 1: Topic Name
**Student's Question**: "What is chain-of-thought prompting?"

**Initial Understanding**: Student familiar with basic prompting but not CoT

**Explanation Given**: Chain-of-thought prompting asks Claude to show reasoning steps...

**Understanding Check**:
- Question asked: "When would CoT be most useful?"
- Student's answer: "For complex math or logic problems"
- Understanding level: Good

**Follow-up**: Practice with coding problems next session

---

## Identified Knowledge Gaps

| Topic | Severity | Notes |
|-------|----------|-------|
| API Parameters | Medium | temperature vs max_tokens confusion |
| Error Handling | High | No agent reliability design experience |

---

## Topics Mastered Today

| Topic | Confidence | Notes |
|-------|------------|-------|
| Tool Use Concepts | Medium | Understands extension capability |
| Agent Loop | Medium | Grasps task decomposition flow |
```

--------------------------------

### Markdown Claude Learning Progress Tracker

Source: https://context7.com/iamewang/cfp-study/llms.txt

This markdown snippet represents the central progress tracker for the Claude learning environment. It summarizes overall progress, domain-specific progress, mastered topics, and areas needing further study, including confidence levels and mastery dates.

```markdown
# Claude Learning Progress Tracker

**Last Updated**: 2026-01-22
**Learning Goal**: Master 8 Claude AI knowledge domains
**Remaining Time**: [days] days

---

## Quick Stats

📊 **Overall Progress**: 4/49 topics covered = **8%**
📚 **Learning Materials**: Official docs, API docs, tech blogs
⏰ **Remaining Time**: [days] days
🎯 **Target**: Master core Claude AI domains

---

## Domain Progress Summary

| Domain | Weight | Topics Covered | Status | Priority |
|--------|--------|----------------|--------|----------|
| **A. Claude Fundamentals** | 20% | 1/6 | 🔵 Started | **High** |
| **B. API & Integration** | 15% | 0/6 | ⚪ Not Started | High |
| **C. Prompt Engineering** | **18%** ⭐ | 0/8 | ⚪ Not Started | **Highest** ⭐ |
| **D. Advanced Features** | 15% | 3/7 | 🔵 Started | **High** |

---

## A. Claude Fundamentals (20%)

### Mastered Topics (1/6)

- [x] **A.6** Claude vs GPT differences
  - **Mastery Level**: Medium
  - **Mastery Date**: 2026-01-22
  - **Key Understanding**: Long context, tool use, comparison with domestic AI
  - **Materials**: Comparative analysis and observation

### Not Yet Learned (5/6)

- [ ] **A.1** Model architecture and principles
  - **Focus**: Transformer architecture, attention mechanism
  - **Key Concepts**: Parameters, layers, inference
- [ ] **A.3** Context window and tokens
  - **Focus**: Tokenization, context limits, cost impact
  - **Key Concepts**: 200K window, token counting, optimization
```

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