Safety, guardrails, security, and sovereignty
In a real deployment, safety is not a box somebody ticks near the end. It is the design, and it decides whether the thing ever gets used. This lesson covers invented answers, deliberate attacks, the controls you wrap around a model, privacy, where the system is allowed to run, and the most credible thing you can say to a sceptic: where you would refuse to use AI at all.
Walk a sceptical stakeholder through the specific risks of a proposal and the specific controls that address each one.
Lessons 1 to 5. Retrieval, agents, and evaluation all show up as safety controls here.
Plain-English mode
Each section opens with a short summary before the detail. Turn this off once the vocabulary feels familiar.
A hallucination is a confident, fluent, false answer. Since the model is built to produce plausible text, this is normal behaviour rather than a fault, and most of it comes from bad source data rather than the model itself.
A is a confident, fluent, entirely false output. Because the model is a plausibility engine at heart, this is the default risk rather than an anomaly, which is why every serious design starts here.
Two distinctions are worth having. An answer can contradict the source you gave it, or it can simply be unsupported by any source at all. Separately, it can ignore the context you provided, or it can be wrong about the world. Different causes, different fixes, and mixing them up sends teams chasing the wrong problem for weeks.
Here is the calming part, and it is the most useful thing in this section. Most hallucinations in production trace back to missing or badly processed source material and to weak retrieval, not to the model deciding to lie. The document was scanned badly, or the passage was split in the wrong place, or the search never found it. Which means most of it is fixed with better rather than a more expensive model.
The mitigations stack rather than compete: retrieve real sources, require citations, measure how faithful answers are to those sources, set a confidence threshold below which the system declines, and put a human on the high-stakes cases. The rest of this lesson is part of the paid course.
Answer in your head first, then reveal. Getting it wrong is the part that makes it stick.
Why is swapping in a more expensive model usually the wrong first response to a hallucination problem?
The rest of this lesson
- People deliberately attacking your system
- Guardrails: the control layer
- Personal data, permissions, and audit
- Where the system is allowed to run
- When not to use AI at all
- One worked example: a government phone line
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