AI literacy & boundaries
Identify suitable tasks, outputs that require checks, data-confidentiality boundaries, and situations better served by ordinary workflows. Exercises do not require participants to upload sensitive data.
Learn to use AI through real team tasks: write clearer instructions, check responses, and build a repeatable working habit. The starting point is your work, not a catalogue of tools or prompts.
Rahman Fakhru · Jakarta, Indonesia
For beginners, professionals, communities, companies, and content or operations teams looking for team AI training, AI workshops, or vibe coding training. Sessions with Rahman Fakhru can be discussed in Indonesian or English, online or in Jakarta subject to the brief and availability. Here, AI trainer means teaching people to use AI, not data labelling or training a machine-learning model.
Identify suitable tasks, outputs that require checks, data-confidentiality boundaries, and situations better served by ordinary workflows. Exercises do not require participants to upload sensitive data.
Practise describing context, goals, examples, constraints, and output format. Compare responses against work criteria rather than treating confident wording as evidence of accuracy.
For suitable learners, build one small prototype or app with AI coding tools from brief through output checks. Nontechnical learners can use simpler workflow examples; agents, skills, CLI, Git, and integrations follow a review of learner readiness.
Agree the learning material, exercises, sample instructions, and evaluation notes to be handed over. Continued coaching, software implementation, and account or API costs are scoped separately.
These open resources show published topics and exercise formats. They are not a training-client list, instructor certification, or a claim of participant outcomes. Team-session material is selected after reviewing the brief. Open lessons retain their original language.
Explore published material on AI, instructions, and technical workflows before choosing a suitable level and topic for your participants.
A learning example about reusable skills and instructions. It is not a required starting level for nontechnical participants.
A reference for learners interested in AI-assisted CLI and Git workflows. Nontechnical sessions can use simpler work examples instead.
Describe participant roles, starting skills, permitted applications, and one task to improve. Use anonymous examples or synthetic data in the initial discussion.
Define topics, level, duration, participant count, location or online platform, account requirements, and exercise outputs. Scheduling and a proposal follow that review.
Participants try examples, inspect weaknesses, and revise instructions. Next steps follow the exercise evidence rather than assuming every task should become automated.
Compare starting and final examples against an agreed rubric: instruction clarity, output accuracy, verification quality, and ability to repeat the process. Time savings or business impact need separate measurement after adoption. This page does not promise a percentage productivity improvement.
Not for AI literacy, prompting, and response evaluation. A vibe coding session can start from a brief and a simple prototype; coding, CLI, Git, agents, or integrations are included only when appropriate to the learners and their goals.
This service teaches people and teams to use AI. Dataset labelling, model-evaluation jobs, pre-training, and fine-tuning are not automatically included in its scope.
In-person Jakarta or online formats can be discussed. Participant count, duration, equipment, data policy, schedule, and travel requirements must be agreed in the brief.
Fees depend on agreed material, format, duration, and follow-up. This service is not marketed as an official AI-vendor certification, and no certification, business outcome, or delivery date is promised before reviewing a proposal.
Describe the need, the current situation, and your intended outcome. Scope and a proposal follow a review of the brief.
Connect applications and repetitive work without giving up control of data and decisions. Start with one useful workflow, not a promise to automate an entire business at once.
Learn to use AI through real team tasks: write clearer instructions, check responses, and build a repeatable working habit. The starting point is your work, not a catalogue of tools or prompts.
Turn spatial needs into design decisions people can understand. Layouts, material direction, and visualisation help compare options before work begins on site.