AI Summary and games
CPD tracking
Fixed hours for this level: 3. Timed assessment time is included once on pass.
View in My CPDThis page is a recap and a play space. It is not new teaching. It is where you test your judgement and turn the ideas into habits.
Big ideas to remember
AI is systems, not magic. A model is a component. The system is everything around it: data, interfaces, monitoring, fallbacks, and ownership. If the system is weak, a strong model just fails faster.
Data quality often matters more than the model. If the data is wrong, missing, or biased, the model will faithfully learn the wrong thing. You can tune parameters for weeks and still be polishing a broken foundation.
Deployment changes everything. Real users behave differently to test data. Latency changes workflows. Drift changes outcomes. In production, the work is observation, iteration, and controlled change.
Common mistakes and myths
Bigger models are not always better. Bigger can mean slower, more expensive, and harder to operate. Sometimes a smaller model with a better data path wins.
Accuracy does not equal success. Accuracy can hide the cost of mistakes. In a spam filter or fraud detector, the trade offs are what matter, not the headline number.
AI does not replace judgement. It can speed up work, but it can also speed up mistakes. If you remove human judgement, you usually remove accountability too.
Mental models that actually help
If you are stuck, return to input, model, output. What inputs does the system really see. What does the model output. What happens next. Many failures are a mismatch between what the team thinks is happening and what is actually wired up.
Think in feedback loops. Models change behaviour, and behaviour changes data. A recommender system shapes what people click. A fraud system changes what attackers try. That loop means yesterday’s evaluation is not a guarantee.
Humans in the loop is a design decision. It only works if humans have context, time, and authority. If they are rushed or powerless, you have a rubber stamp, not oversight.
A practical mental model
Follow the data and the decisions.
Inputs
What the system actually receives.
Data quality and context.
Model
A component that maps inputs to outputs.
Limits and uncertainty.
Decisions
How outputs change actions.
Guardrails and ownership.
Games and challenges
These are light practice prompts. The goal is to recognise patterns quickly and explain your reasoning in plain language.
Recap quiz
Why does deployment change how a model behaves
Why can accuracy hide problems
Scenario: A team says 'the model is smart, it will figure it out'. What is a sign of magic thinking
What is one reason a smaller model can win
Scenario quiz
A spam filter is blocking important emails. What should you check first
A fraud model was stable for months and then missed a new attack pattern. What likely changed
A tool shows a risk score but nobody can explain it. What is the governance problem
A model is expensive and slow. What is one safe way to reduce cost without redesigning everything
Final reflection
What kind of AI would you trust. Be specific about context, constraints, and consequences.
Where would you never deploy AI. Think about power, harm, and irreversible decisions.
When should you not automate. Name the signals that tell you a human must stay in control.