Learn to use AI wisely. Build useful things. Stay in control.
AI Life Learning helps non-technical people understand how AI works, use it with better judgment, and create practical workflows for learning, work, planning, and development.
For people who want to build software with AI, the Build with AI pathway adds the software and infrastructure foundations required to create, deploy, and maintain a working prototype.
Having the tools is not the same as knowing how to use them.
Plenty of capable people now have a paid AI account and still have no reliable way to work with it. Access is not capability, and the gap between the two is where the frustration lives.
Inconsistent results
Sometimes the answer is excellent and sometimes it is confidently wrong, and it is not obvious which one you are looking at.
Uncertain boundaries
It is unclear what is safe to share, what should stay private, and what you remain responsible for.
No repeatable method
One good result is an accident. Being able to get that result again is a capability, and it does not arrive by itself.
AI suggests and assists. You define, evaluate, and decide.
The point of this programme is to make you more capable, not more dependent. That distinction shapes everything in it.
AI can explain, draft, summarise, question, and generate. What it cannot do is decide what problem is worth solving, what evidence is sufficient, what should be accepted, or when the result is good enough. That work stays with you, and doing it well is a skill you can develop.
Two pathways, depending on what you're trying to do.
They are different offers for different situations, not beginner and advanced. Pick the one that matches the problem in front of you.
AI Life Skills
For people who want to use AI for learning, research, writing, planning, communication, and structuring decisions.
One useful AI-supported workflow you can run again on your own.
- Judge when AI is the right tool for a task, and when it is not
- Give it the context it actually needs
- Check an answer before you rely on it
- Recognise what should not be shared with a tool
- Turn one good result into a method you can repeat
Build with AI
For people with a real idea for a tool who lack the software foundations to turn it into something that works and keeps working.
One deployed prototype you can explain, run, test, change, and redeploy.
- Explain in plain language what your application is and how it works
- Describe its main parts and where each one runs
- Run it locally, and deploy the version other people use
- Know where your data lives and what happens when a service fails
- Make a second change and ship it without help
- Estimate what it costs to operate, and spot what it does not need
You leave with something that works, and the understanding to keep it working.
Start from your real situation, not from the tools.
Every session works on something you actually need, in small steps you verify as you go.
- 01
Start with your real situation
Not a demo problem. Something you genuinely need to move forward on.
- 02
Separate the problem from the assumed solution
Most stuck projects are solving a solution rather than a problem.
- 03
Define the smallest useful outcome
Decide what finished looks like before you start building towards it.
- 04
Build or apply in small, testable steps
Incremental work you can check beats a large result you cannot inspect.
- 05
Verify before you rely on it
Treat every output as a proposal until you have actually checked it.
- 06
Keep the method, not just the result
You should finish able to do it again without the session.
From “I want a booking tool” to something deployed and understood.
A working session with a non-technical professional who wanted clients to book time without the back-and-forth. They already had a paid AI account. What they did not have was any way to direct or own the build.
- 01
Define the real job
Help a client find and reserve a time. Not: rebuild an existing scheduling product.
- 02
Challenge the assumed solution
The first idea was far larger than the problem required.
- 03
Reduce the scope
Find the smallest flow that genuinely solves it.
- 04
Map the system
What the parts are, where each one runs, what moves between them.
- 05
Build one part at a time
Run it locally before making it public.
- 06
Deploy, then change it
Making a second change is what proves the ownership transferred.
This is an illustration of the learning problem, drawn from one working session. It is not evidence that the programme or the prototype has been commercially validated.
No sessions are scheduled yet.
The programme is being shaped now, and the first sessions have no dates. Rather than publish a schedule that does not exist, this page will show real dates, formats, and durations when there are some. If you want to know when that happens, tell us which pathway interests you.
When your situation needs more than a session.
Some situations need direct help rather than a programme: an unclear AI opportunity that needs turning into something simple, tested, and maintainable, or a prototype that has grown complicated enough to need a second pair of eyes. This is not general AI advice, and it is not always the right answer. If it might fit, say so in the form below and describe the situation.
What this is, and what it is not.
The principles below govern the whole experience.
AI does not make important decisions for you, and nothing it produces becomes part of your plan without your say-so.
Treat what AI produces as a proposal until you have checked it. Confidence in the writing says nothing about accuracy.
Knowing what not to put into a tool is part of using it well, and it is something the programme teaches directly.
A prompt without a purpose is how scope and cost quietly grow.
A workflow or prototype you cannot undo is one you do not really control.
Recognising the limit of your own judgment is a skill, not a failure.
Its main parts, where they run, what it depends on, and what it costs to operate.
The programme builds your capability. It does not tell you what to decide.
Nothing here is delivered to you while you watch. You do the work; the programme gives it structure.
You use AI to build the solution. It is not left to build the solution for you.
- Not therapy or diagnosis
- Not legal advice
- Not tax advice
- Not regulated financial or investment advice
- Not autonomous decision-making
- Not professional software engineering training
- Not a guarantee of career, financial, or business outcomes
- A qualified developer
- A security specialist
- A legal professional
- A medical professional
- A regulated financial adviser
Related to In 20, but not a route into it.
AI Life Learning comes from In 20, a personal AI life planning platform. The two share a way of thinking, that AI should make you more capable rather than more dependent, but they are separate offers.
AI Life Learning develops the capability to use AI well. In 20 is one optional environment for applying that capability to longer-term planning and decisions. Taking part in one does not commit you to the other, and nothing here will move you into In 20 without your asking.
Read about In 20 →Tell us which pathway fits your situation.
There are no dates yet. Leaving your details means we will tell you when there are, and it tells us which of the two pathways people actually want, which is genuinely useful while this is being shaped.