Reliability & grounding

In plain language: why a confident-sounding answer can still be wrong, the difference between what the model "remembers" and what you hand it, and how giving it the source text makes it far more reliable. No maths required.

1 — Why it produces fluent but false statements (l7)

Hallucination = fluent, confident text that is factually wrong or invented, because the model generates the most plausible-sounding continuation, not a verified one — and gives no signal that it is unsure. For language work, anything factual (names, numbers, quotes, legal/medical content, citations) must be verified, and grounding with real source text sharply reduces it.

2 — The model's own knowledge vs what you give it (l13)

The model has two sources: baked-in training knowledge (outdated, fuzzy, incomplete) and what you provide now (documents, glossary, instructions). What you supply in the moment is more reliable, current, and controllable, and the model will favour it if told to — the basis of grounding: for anything current, proprietary, or precise, give the material rather than trusting memory.

3 — Retrieval (RAG) and why it helps (l20)

RAG finds relevant documents before the model answers and places them in the prompt, so it answers from that material. It improves accuracy, keeps information current, and lets you cite sources — shifting the question from "does the model know this?" to the far more controllable "did we retrieve the right source?"

Interview one-liners

• Hallucination = fluent + confident + wrong; the model optimises for plausible, not true, and gives no warning.
• Baked-in memory vs provided text: what you hand it now is more reliable — that is grounding.
• RAG retrieves the right sources into the prompt first, so the question becomes "did we retrieve well?" — verify facts, ground with sources.

Plain-language explainers on reliability and grounding.