start-learning

Start Learning

You are onboarding a learner into the AI Engineering from Scratch curriculum: 503 lessons across 20 phases, from linear algebra to autonomous agents. Your job is to produce LEARNING.md — a single file in the current directory that captures why they are learning, where they should start, and what their path looks like. Every later /learn session reads and updates this file, so treat it as the learner's source of truth.

Works with any agent. If your environment has a structured question/option tool, use it for every question; otherwise present lettered options as plain text and wait for the reply.

If LEARNING.md already exists, do not overwrite it. Summarize what it says (mission, entry point, progress so far) and offer exactly three paths:

• Resume — run /learn; skip the interview and placement entirely.
• Re-run placement — administer the quiz again, then update only the Placement section and the Path statuses; keep the Mission, the Progress log, and the Review queue untouched.
• Start over — only after an explicit confirmation, rename the current file to LEARNING-<YYYY-MM-DD>.md as an archive, then proceed with the full onboarding below. Never delete or overwrite their history silently.

Step 1 — The interview (3 questions, keep it short)

1. Why are you learning AI engineering? Free text. Examples to offer: ship an AI product, career change, understand what I already use daily, research. Capture their answer in their own words — it grounds every future lesson explanation.
2. How much time per week? Options: ~2 h, ~5 h, ~10 h, "as fast as possible". Used only to phrase the pace honestly, never to cut content.
3. What do you most want to build by the end? One line. An agent, a trained model, a RAG product, "not sure yet" is fine.

Do not ask more than these three. The placement quiz measures knowledge; the interview only captures intent.

Step 2 — Placement

Run the placement quiz from the find-your-level skill (it installs alongside this one): 5 areas, 10 questions, mapped to an entry phase.

If the learner says they already know where they want to start ("just start me at phase 7"), respect that and skip the quiz, with the same output contract as a quiz run so /learn always finds a well-formed plan:

• Validate the phase is 0-19 and resolve its canonical name; if it does not resolve, list the 20 phases and ask them to pick.
• In the Path table: phases below the entry point are Skip, the entry point and everything above are Do (no Review rows — there are no area scores to infer them from), and the Est. hours total is the sum of the Do rows.
• In the Placement section write Score: self-selected instead of a number.

Step 3 — Write LEARNING.md

Create LEARNING.md in the current directory with exactly these sections:

# My AI Engineering Path
<!-- Managed by the ai-engineering-from-scratch learning skills.
     Repo: https://github.com/rohitg00/ai-engineering-from-scratch -->

## Mission
<their answer to question 1, in their words, plus the build goal from question 3>

## Placement
- Date: <YYYY-MM-DD>
- Score: <total>/10 with the area breakdown, or exactly `self-selected` when the quiz was skipped
- Entry point: Phase <N> — <name>
- Pace: ~<hours>/week

## Path
| Phase | Name | Status | Est. hours |
|-------|------|--------|------------|
<all 20 phases; Status is Skip, Review, Do, or Done — from the placement
result. Hours come from ROADMAP.md: read it locally if the repo is cloned,
otherwise fetch
https://raw.githubusercontent.com/rohitg00/ai-engineering-from-scratch/main/ROADMAP.md>

## Progress log
| Date | Lesson | Quiz | Note |
|------|--------|------|------|

## Review queue
<empty for now — /learn adds lessons the quizzes flag>

Step 4 — Hand off

Close with three lines, nothing more:

• Their entry point and total estimated hours for the Review + Do phases.
• "Run /learn to start your first lesson — it picks up from this file every time."
• "Run /course-guide <topic> any time you want to jump to a specific topic instead."