Session Review

Ask open-ended questions. Don't rush through a checklist — follow their energy:

If they seem excited:

• "What clicked for you?"
• "What made that moment feel good?"
• "Did anything surprise you?"

If they seem frustrated:

• "Where did you get stuck?"
• "What were you trying to understand that wasn't working?"
• "Was the lesson too hard, or was it something else?"

If they seem neutral:

• "What did you notice?"
• "Did anything feel different from last time?"
• "Was there a moment where you had to think hard?"

3. Dig Into Struggles

Struggles are valuable data. When they mention something hard, explore it:

• "What specifically was hard about it?"
• "Did you try different approaches?"
• "Do you have a theory about why it wasn't clicking?"
• "Is this something you want to work on more, or move past?"

4. Surface Curiosities

Find out what they want to learn next:

• "Is there anything you're curious about now?"
• "Did this session make you want to try something specific?"
• "What would make the next session feel productive?"

5. Close the Loop

Summarize what you heard and confirm:

So it sounds like:
- The basic concept is starting to make sense
- The math notation is still tricky
- You're curious about how to implement this from scratch

Does that capture it?

Saving the Session

After the conversation, save a Markdown file to src/data/sessions/.

File Naming

Use the format: YYYY-MM-DD-<slug>.md

If multiple lessons or a general session: YYYY-MM-DD-session.md

Examples:

• 2026-01-17-attention-mechanism.md
• 2026-01-17-session.md

File Format

# Session: [Lesson Title or "Study Session"]
Date: YYYY-MM-DD
Lesson: [lesson-slug or "multiple" or "exploratory"]
Duration: [if mentioned]

## Summary
[2-3 sentence summary of what happened]

## What Worked
- [Bullet points from conversation]

## What Was Hard
- [Bullet points from conversation]
- [Include specific details that might inform future lessons]

## Curiosities
- [What they want to learn more about]
- [Questions that came up]

## Insights
[Any patterns you noticed, connections to previous sessions, or observations
that might be useful for lesson planning]

## Next Steps
- [Specific things to try next time]
- [Lessons that might address struggles]

Example

# Session: Attention Mechanism
Date: 2026-01-17
Lesson: attention-mechanism
Duration: ~30 min

## Summary
First deep dive into the attention mechanism. Got the intuition for queries, keys,
and values but struggled with the matrix math notation.

## What Worked
- The analogy to database lookups clicked immediately
- Interactive visualization helped see what softmax does
- The "why" explanation was motivating

## What Was Hard
- Matrix multiplication notation (transposing K)
- Not clear why we divide by sqrt(d_k)
- Implementing from scratch in PyTorch

## Curiosities
- How does multi-head attention work?
- Why do we need position encoding?
- How is this different from RNN attention?

## Insights
Ready for a lesson on multi-head attention. The single-head concept is solid now,
but the matrix notation needs more practice.

## Next Steps
- Try implementing attention from scratch in PyTorch
- Watch 3Blue1Brown video on matrix multiplication
- Move to multi-head attention lesson

Reading Previous Sessions

Before starting a review, check for recent sessions:

ls -la src/data/sessions/

Reference previous sessions in the conversation when relevant:

• "Last week you mentioned X was hard — how is that feeling now?"
• "You were curious about Y — did you explore that?"

Updating Learner State

After saving the session, consider updating src/data/learner-state.ts if:

• A skill level has clearly changed
• A new struggle has emerged
• A previous struggle has been resolved
• A strong preference has been discovered

Don't update on every session — only when there's a meaningful shift.

Tips

• Follow their energy. If they want to vent about frustration, let them. If they're excited, celebrate with them.
• Don't interrogate. This is a conversation, not a form.
• Capture specifics. "The math was hard" is less useful than "the matrix transpose in Q·K^T was confusing."
• Connect sessions. Reference what you know from before. Show that this is a journey.
• End with direction. They should leave knowing what to try next.