Quality and Risks
Hallucinations, non-determinism, weak spots of semantic search — the reasons AI gets things wrong. These articles explain where the errors come from and how to keep them under control.
Source citation: how to verify a model answer in a minute
Source citation means a link to a specific fragment of a document next to every claim a model makes: the answer can be verified in a minute and is fit to act on.
Knowledge Cutoff — What It Means in Plain Terms
A knowledge cutoff is the date after which a model knows nothing about the world: anything more recent has to be supplied along with the request.
Non-determinism: what it means in plain terms
Non-determinism is a language model's tendency to answer the same request differently each time; the spread is narrowed by a fixed response format, a quality threshold, and sample-based acceptance testing.
What context rot is and how to hold quality steady
Context rot is the drop in answer accuracy as the volume of supplied text grows: the window is not yet full, but the detail that matters is already getting lost.
AI hallucinations: why models confabulate and how to catch it
A hallucination is a confident answer that looks plausible but does not match reality: an invented link, a made-up number, or a clause that appears in no regulation.