Why do LLMs hallucinate and how do you reduce hallucinations?
mediumAnswer
- LLMs generate the most probable next token — they don't have a truth check.
- Hallucinations arise when the model lacks knowledge, when the prompt is ambiguous, or when it 'commits' to a plausible-sounding continuation.
- Mitigations: retrieval-augmented generation (ground in cited sources), lower temperature and constrained decoding, explicit 'say you don't know' instructions, tool use, self-consistency and verifier models, and evaluation with factuality benchmarks.
Check yourself — multiple choice
- Hallucinations disappear with higher temperature
- RAG grounds outputs in retrieved sources and reduces hallucinations
- LLMs internally verify facts before answering
- Setting prevents hallucinations
Retrieval-augmented generation with cited context is the main practical mitigation.
#hallucinations#reliability
Practise LLMs & GenAI
214 interview questions in this topic.
Related questions
- What is 'honesty' as an alignment target and how do you train for it?
- Name three types of LLM hallucination.
- How do you detect hallucinations in production?
- What is sycophancy in LLMs and how do you reduce it?
- What is faithfulness in RAG and how do you measure it?
- How do you make an LLM cite its sources reliably?