A field profile tells the mentor what professional competence looks like in a specific field: the concepts, the misconceptions, the authoritative sources, and the capstone that proves mastery. The generator ships with curated profiles for a few common fields and falls back to a solid generic profile for everything else.
- quantum computing —
quantum-computing - Rust —
rust - organic chemistry —
organic-chemistry - machine learning —
machine-learning(also matches "AI") - macroeconomics —
macroeconomics - contract law —
contract-law
List them anytime:
mentor fieldsCreate references/fields.json. The generator merges it over the built-ins at
startup. Keys are arbitrary; each value uses this schema:
{
"my-field": {
"key": "the Human-readable field name",
"aliases": ["my field", "alternate spelling", "abbreviation"],
"mentor_name": "Dr. Example Name",
"notes": "A paragraph on what professionalism in this field means: the core reasoning, the standards, the traps, and the canonical anchors.",
"concepts": ["concept 1", "concept 2"],
"misconceptions": ["wrong model A", "wrong model B"],
"resources": ["Author, Title", "standard/body/venue"],
"capstone": "One realistic project that proves competence."
}
}The notes paragraph is the highest-leverage part. A strong profile:
- Names the field's foundational reasoning and demands it first.
- States the standards a professional is held to.
- Names the classic traps learners fall into.
- Points at real authorities (textbooks, standards bodies, journals).
The resolver checks, in order: exact key / alias match, then substring match, then falls back to the generic profile. Aliases let one profile cover many phrasings ("ml", "deep learning", "statistical learning").
For fields without a profile, the generator builds notes that force the mentor to establish vocabulary and models first, then tools and workflows, then applied judgement — while distinguishing consensus from debate and attacking misconceptions. This is deliberately field-agnostic but pedagogically strong.