Topic
Hallucination in LLMs
11 posts on hallucination in llms — part of glossary on the n4n AI blog.
Why do LLMs hallucinate? The technical reasons
A deep technical breakdown of why LLMs hallucinate, covering training objectives, probability distributions, and architectural constraints that make fabrication inevitable.
How to reduce hallucinations with RAG
A step-by-step guide to building a RAG pipeline that measurably reduces LLM hallucinations, with runnable code for retrieval, reranking, and evaluation.
How to detect AI hallucinations in production
A step-by-step guide to detecting LLM hallucinations in production systems, from ground truth construction to online monitoring with runnable code.
Hallucination vs confabulation: is there a difference
A precise technical distinction between hallucination and confabulation in LLMs, with concrete examples and engineering implications for building reliable systems.
Hallucination in code generation: when AI invents APIs
Understand hallucination in AI code generation — why models invent fake APIs, how to detect it, and practical strategies to prevent broken code from reaching production.
Hallucination benchmarks: how models are scored
A technical explainer of hallucination benchmark scoring models — how they work, what they measure, and why they matter for production LLM systems.
Grounded generation: reducing hallucination with context
Grounded generation ties LLM outputs to verified context, reducing hallucination. Learn how it works, why it matters, and common pitfalls to avoid.
GPT-4 vs Claude: which hallucinates less
A technical comparison of GPT-4 and Claude hallucination behavior across coding, reasoning, and retrieval tasks with practical guidance for model selection.
Citation hallucination: when AI invents fake sources
Understand AI citation hallucination — when models fabricate plausible but nonexistent sources — with causes, detection strategies, and mitigation techniques for production systems.
Can AI hallucinations ever be fully eliminated
A practitioner's analysis of whether LLM hallucinations can be fully eliminated, examining root causes, mitigation strategies, and fundamental limits.
AI hallucination explained: why LLMs make things up
A practitioner's breakdown of why LLMs hallucinate — the mechanism, the risks, a concrete failure mode, and what engineers get wrong about mitigation.
More topics in glossary
- Structured Outputs & JSON Mode19
- AI Agents Fundamentals12
- Sampling Parameters: Top-p, Top-k & Penalties11
- Context Window & Context Length10
- Fine-Tuning Fundamentals9
- Foundation Models: Base vs Instruct vs Chat9
- Model Families & Naming Conventions: GPT-5, Claude, Gemini 3, Llama 4, Mistral, DeepSeek, Qwen, Grok9
- Grounding & Fact-Checking in AI8
- LLM Benchmarks: MMLU, HumanEval, SWE-bench & GPQA8
- Max Tokens, Stop Sequences & Output Truncation8
- Quantization Formats: GGUF, GPTQ, AWQ & INT4/INT88
- Chain-of-Thought Prompting7