Why "sounds right" doesn't mean "is right"
A language model – as shown in module 2 – predicts the most likely next word, it doesn't check facts against a database. That's why it can phrase wrong information just as fluently and convincingly as correct information. This effect is called "hallucination".
The four key limits and risks
Hallucinations
Invented but convincing-sounding facts, sources, or numbers – without the model itself "noticing" that it's wrong.
Bias
A language model learns from vast amounts of human-generated text – including its prejudices and blind spots. These distortions can surface in answers without anyone noticing.
No real understanding
A language model recognizes patterns in language, it doesn't understand or judge like a human. On unusual or ambiguous questions, it lacks genuine contextual understanding.
Privacy & confidentiality
What's typed into a prompt may, depending on the provider, be stored, used to improve the model, or seen by staff. Confidential customer or business data doesn't belong in public AI tools without checking first.
Why this matters for you as a decision-maker
These limits aren't an argument against using AI – they're an argument for using it deliberately: have humans double-check results on important decisions, ask vendors about data privacy and processing, and deploy AI where mistakes are noticeable and correctable. That's exactly the idea behind Beyond Prompt's automations: AI as a tool within a process, not an uncontrolled black box.