Get genuinely productive with everyday AI — ChatGPT, Claude and Copilot — for the real tasks in your week. A practical half-day for professionals, no coding.
Most people use these tools at a fraction of their potential. This half-day fixes that.
This practical half-day fixes that: the prompts, patterns and workflows that actually save time on the real work in your week — writing, analysis, planning, email — across ChatGPT, Claude and Microsoft Copilot.
No coding, no hype. For any professional who wants to stop falling behind and start getting hours back.
Taught by practitioners who use these tools every day.
We work on the tasks in your week, so what you leave with is built around your job rather than someone else's.
Most of the value is learning to tell a good answer from a fluent one. That judgement is the point of the day.
Not a policy document. One line, simple enough to use without asking permission every time.
Movement I · Block 01Find the timeAn honest map of your week, sorted by what a model can and cannot do.
Movement II · Blocks 02–03Build the habitPatterns that hold up across assistants, applied to your own material.
Movement III · Block 04Stay safeWhat never goes into a public model, in plain terms.
Where AI actually saves you timeWe map the recurring tasks in your week and sort them honestly: the ones a model does well, the ones it does badly but confidently, and the ones not worth handing over at all.
An honest map of your week, sorted by what a model can and cannot do.
Prompting that survives real workThe handful of patterns that hold up across ChatGPT, Claude and Microsoft Copilot — context, constraints, worked examples, and asking for the reasoning before the answer.
A handful of patterns that work the same whichever assistant you are given.
Your week, rebuiltWriting, analysis, planning and email reworked live on your own material. You leave with prompts built around your job, not a transcript of someone else’s.
A working set of prompts built around the tasks you actually repeat.
What not to paste inConfidentiality, client data and the National AI Centre’s AI6 practices in plain terms — a rule simple enough that you can apply it without asking permission each time.
A one-line rule for what never goes into a public model.
You leave with a working prompt set built around the tasks you actually repeat — written on your own material during the session, not copied from a handout.
Alongside it: a one-line rule for what never goes into a public model, simple enough that you can apply it without asking permission each time.
No coding and no preparation required. Bring a laptop and access to whichever assistant your organisation already uses — ChatGPT, Claude or Microsoft Copilot.
It helps if you arrive with two or three real tasks from your own week. We work on those rather than on invented examples.
Frameworks are great. But understanding what they abstract — the loop, the parsing, the failure modes — is what lets you actually debug and design them under pressure.
Amir founded, funds and directs AI Tech Institute, and still teaches. He holds a PhD and has spent more than twenty years building and leading AI, machine learning and data teams in Australian industry — the kind of work the programs here are drawn from rather than adapted to.
He currently leads asset performance and analytics at Synergy, Western Australia's state-owned electricity generator and retailer. Before that he led GenAI and MLOps program delivery at EY, working on platform modernisation for tier-one Australian banks; served as Chief AI Engineer at Hancock Prospecting, where he built enterprise GenAI platforms for executive decision support, led a Snowflake migration, stood up on-premises NVIDIA H100 infrastructure and Kubernetes clusters for production AI, and built a team of twelve engineers; and spent nearly five years as Tech Lead for ML and Data Science at Woodside Energy.
He has been teaching for twenty-one years. He is an Adjunct Associate Professor at the University of Western Australia, teaching quantitative analysis and decision-making at master's level, and holds Databricks University Alliance Faculty status and the AWS Certified AI Practitioner certification. He has published peer-reviewed research in Exploration Geophysics on machine learning for reservoir characterisation, and writes and records openly about the parts of AI engineering most courses skip — agent loops, termination logic and failure modes.
Cohorts are deliberately small so every piece of work gets reviewed properly and every presentation gets heard in the room. If an intake is full we’ll say so and hold your place for the next one — we won’t quietly oversell a cohort.
See all upcoming dates →The prerequisites for this program are in “Who it’s for” above. Our foundation-level programs and workshops assume none. If you’re unsure, ask on a qualification call — we would rather redirect you than take your money for the wrong cohort.
The commitment for this program is shown in the hero and in “Who it’s for”. Cohort programs run live sessions plus practice between them; workshops are a single block with no homework.
The work you shipped — defended in front of the cohort — and an honest recommendation on your next step. Sometimes that is another cohort with us, sometimes it isn’t; we will tell you which.
No. AI Tech Institute is not a Registered Training Organisation and this is not a nationally accredited qualification. It is professional education, assessed on the work you ship, with a certificate of completion from us. We won’t claim accreditation we don’t have.
Yes — we invoice organisations directly and can supply a scope and outcomes summary for your L&D or capability budget. If three or more people from one team want in, talk to us about a private cohort instead.
Full refund any time before the program starts. Once it has started we can’t refund the place, but we will carry you into a later intake where the circumstances warrant it. Ask us — we’re reasonable.
Thirty minutes with us and you’ll know whether this is your right next step — or which of our programs is. No pitch, no payment, and we will say so if the answer is “not this one”.
