A school built around
how developers actually learn
Promptly was put together by practitioners who wanted a structured way to teach AI development — not a marketing-heavy course platform, but a cohort school with real assignments and code review.
Back to HomeAbout Promptly
Promptly started in Kuala Lumpur as a response to a specific problem: developers wanted to work with AI models but found that most available learning materials were either too shallow or too academic. There was no middle path — nothing that combined real engineering structure with the kind of models people actually use at work.
The school was set up to fill that gap. The founding team came from software development and data engineering backgrounds and had spent years reviewing code, running technical interviews, and watching the same gaps appear repeatedly in candidates who had completed courses elsewhere. The conclusion was straightforward: the issue was not content coverage but the absence of structured practice with feedback.
Promptly's cohort model is built around that insight. Every participant completes assignments, submits them for review, and receives written feedback from an instructor. The curriculum covers what working developers actually encounter — environment setup, model APIs, data preparation, evaluation, reproducibility, and engineering habits — not a theoretical overview of algorithms that are unlikely to appear in day-to-day project work.
The school operates from Pavilion Damansara Heights in Kuala Lumpur and runs cohort intakes throughout the year. Programmes are part-time and structured to fit around existing employment. The three tracks vary in scope and depth, from a five-week introduction to a thirty-two week engineering programme.
The Team
Ahmad Zulkifli
Eight years in backend engineering with a focus on data pipelines and model deployment. Designs the curriculum for the 14-week and 32-week tracks.
Nurul Rashidah
Technical writer and former ML engineer who translates complex tooling concepts into structured written guides and project briefs used across all tracks.
Tan Kah Meng
Works primarily on participant submissions for the open models cohort — reviews code, writes feedback, and flags common structural issues in project repositories.
Standards & Practices
Structured code review
Every assignment receives written instructor review. Feedback covers code structure, documentation, and approach — not just whether the code runs.
Data privacy compliance
Participant data is handled in accordance with Malaysia's Personal Data Protection Act (PDPA 2010). Enrolment data is not shared with third parties for marketing.
Honest scope documentation
Each programme page states what participants will cover, what they submit, and what Promptly documents at the end. No ambiguity about scope or outcomes.
Reproducibility standards
Participants are taught and assessed on reproducibility — environment files, dependency management, and documented run instructions are part of every submission.
Small cohort sizes
Cohort intake is limited so reviewers can provide meaningful feedback on each submission. Larger numbers would reduce review quality — a tradeoff Promptly does not make.
Written learning materials
Course guides are written documents, not collections of video links. Participants can read, search, and reference materials at their own pace throughout the cohort.
Approach and Values
AI development as a practical skill set has expanded significantly over the past few years, and the range of open-source tooling available to working developers is now substantial. Promptly's position is that working with these tools requires the same engineering habits as any other software discipline — clear dependency management, testable code, readable documentation, and structured version control.
The school does not position itself as a path to employment or as a credential provider. Participants who complete a programme receive a record of what they completed and a repository of the work they produced. What they do with that is their own decision.
Content is updated between cohort intakes to reflect changes in the tooling landscape. The curriculum team monitors which open-source libraries and model APIs are seeing active development and adjusts the coursework accordingly. Participants work with tools that are currently maintained and actively used.
The school values directness. Programme pages describe the workload, prerequisites, and scope plainly. Where a programme does not cover something — employment outcomes, credential recognition, post-cohort job placement — that is stated clearly rather than left ambiguous.
Interested in joining a cohort?
Send an enquiry and someone from the team will respond within one working day with intake dates and programme details.
Contact the Team