Six things that matter when choosing a course
Written, specific feedback
Every reviewed artefact gets written comments tied to what you actually produced. Not a score. Not "good work." Something you can act on.
Work-order structure
Each unit states inputs, method, output and hours before you start. You know exactly what you are taking on.
Honest prerequisites
Each course lists what you need to know, plainly. The six-month programme includes a technical conversation before enrolment confirms.
One-to-one office hours
Office hours in the practitioner programme are individual slots. If you are stuck on something specific, the answer is specific — not a group FAQ.
Left out on purpose
Every course page lists topics it deliberately does not cover. This prevents enrolling on a false premise.
Taught by working engineers
Teaching staff work in the field currently. The content stays close to practice because that is where the teachers spend their time.
What each benefit means in practice
Expertise in production AI work
The instructors have built things in production and carry that into their teaching. The failure taxonomy material in the prompt engineering course comes from a real incident log. The retrieval systems content in the practitioner programme was written by someone who runs them. This is not a universal feature of AI education.
Process you can reuse
The reading protocol from the paper-reading course is yours to keep. The prompt-versioning reference repository and regression harness template from the engineering course go into your own repositories. Artefacts from the practitioner programme are in repositories you own. The deliverables are designed to be reused, not to demonstrate completion.
Support that answers the actual question
Cohort channels provide day-to-day contact. Individual office hours provide focused time on specific problems. Written review provides the most transferable form of feedback. These are three different things, and all three are included where they are relevant to the course.
Transparent fees
All fees are listed on the course pages before you contact us: RM 640, RM 1,780, RM 4,700. The practitioner programme includes compute credit. There are no hidden materials costs, no upsells, and no payment is collected before enrolment is confirmed.
Outcomes that are honest to describe
Modelcraft makes no claim about employment or earnings. What we can describe honestly: you will have worked through a body of material with a practising engineer watching and commenting, and you will have produced artefacts you can show to someone who asks what you can do. That is what the courses produce.
How this compares with other approaches
| FEATURE | TYPICAL AI COURSE | MODELCRAFT |
|---|---|---|
| Feedback on your work | ✗Automated quiz scores | ✓Written line-by-line review |
| Prerequisites stated | ✗Vague or absent | ✓Listed plainly on every course page |
| Office hours | ✗Group forum or none | ✓One-to-one slots (practitioner programme) |
| Scope honesty | ✗Everything sounds covered | ✓"Left out on purpose" section on each page |
| Teaching staff | ✗May not work in the field | ✓Practising engineers, currently working |
| Deliverables you keep | ✗Completion certificate | ✓Reusable protocols, repos, artefacts |
| Fees stated upfront | ✗Often hidden until checkout | ✓Listed on every course page in RM |
What only Modelcraft does
Work-order format for every unit
The work-order structure — input, method, output, hours — runs through every unit of every course. It is not a framework used by other schools and it comes from the instructors' own engineering practice.
Pre-enrolment technical conversation
The six-month programme involves a short technical conversation before a place is offered. This is so both sides can decline without awkwardness — and because a poor fit wastes twelve weeks of your working life.
Incident log as course material
The failure taxonomy in the prompt engineering course is drawn from a real incident log kept by one of the instructors. This is not case-study pedagogy; it is the actual record.
Team cohort option with your own product
The prompt engineering course can run as a team cohort where participants work on their own product rather than a toy example. Reviews are written against your actual codebase, not a generic starter project.
Where we are now
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