Trust the answers
Know how an AI app goes wrong, leaks or is tricked, and the plain fix.
Why does AI make things up?
A language model writes the likely next words, not checked facts. Where its training text says little, a fluent answer can be wrong, and it rarely says so.
How do I check my AI's answers with a small test set?
Keep about 20 questions with known answers and run all of them after every change. A fix for one answer can break others that the score alone hides.
What is prompt injection, and can a prompt stop it?
A web page, email or file can hide text that an AI app takes for orders. Words in a prompt do not reliably stop it; limits in code cap the harm.
How does a safety filter decide, and where does it fail?
A safety filter scores each message and blocks it at a threshold. Every threshold misses some harm or blocks some harmless messages, and rare harm changes the mix.
Can one user's chat leak into another's?
It can, but rarely through the model. Leaks come from a shared cache whose key omits who asked, from logs, or mixed-up requests. Put the user in the key.
Is it safe to download a model file?
Some model files are programs in disguise: opening an old PyTorch file can run code. Files that hold only numbers are built not to. Here is how to tell them apart.