A thread you can test
Hallucinations & Four Mitigations
6 notes move from the word to a real choice at work — understand it first, then decide whether to use it.
Each note stands alone, or becomes the next step in this thread.
THE QUESTION THIS PAGE ANSWERS
ANSWER FIRSTWhat is Hallucinations & Four Mitigations, and which AI decisions does it change?
Three classic types: factual errors / confident fabrication / knowledge cutoff This page keeps the related concepts, common mistakes, and practical notes in one reading thread.
First decide whether you are blocked by a definition, a choice, or verification; then choose the closest of the 6 notes below.
Start with “LLM Hallucination Demo,” then restate the conclusion using your own task.
Do not treat every method in a topic as interchangeable. The answer changes with the input, risk, and acceptance bar.
THIS QUESTION THREAD
Put the word back inside the choice it changes.
LLM Hallucination Demo
Three classic types: factual errors / confident fabrication / knowledge cutoff
Mitigation 1: Prompt Engineering
Constraint instructions + limitations: the model doesn't know what it doesn't know
Mitigation 2: RAG — Retrieval-Augmented Generation
Inject real documents into the context; 5-step animated flow; compare with and without RAG
The Cost & Optimization of RAG
Cost analysis table + four strategies: keyword trigger / model routing / semantic cache / precise chunking
Mitigation 3: Temperature & Top-P
Drag the slider to see probability distributions and output changes in real time
Mitigation 4: Evaluation + Human Review
External correction layer — a cold-start fallback strategy (HITL)