Hallucination
When a language model produces fluent, confident text that is not supported by any source and is often simply false. Retrieval, grounding, and evaluation are the main defenses.
What is Hallucination?
When a language model produces fluent, confident text that is not supported by any source and is often simply false. Retrieval, grounding, and evaluation are the main defenses.
Hallucination is a advanced concept that sits in the LLM and GenAI Ops area of system design. Engineers reach for it whenever they need to reason about real-world trade-offs in that space, not just for textbook correctness, but because real production systems at companies like Netflix, Amazon, and Google make these decisions every day.
If you want to go deeper than this definition, with diagrams, code, and a quiz to lock it in, work through the "LLM Guardrails and Safety" lesson linked below. It walks through the why, the mechanism, the trade-offs, and how the giants actually use it in production.
Learn Hallucination in depth
Full interactive lesson with diagrams, code examples, real-world references, and a quiz.
Open the LLM Guardrails and Safety lessonSee also
Related glossary terms you might want to look up next.
RAG (Retrieval-Augmented Generation)
A pattern that retrieves relevant text from an external index and puts it in the model's prompt so the answer is grounded in current, verifiable facts rather than the model's memory.
Guardrails
The checks around a model that screen its input for abuse and its output for grounding and safety, plus limits on what its tools can do. Containment, not just filtering, is what actually holds.
LLM-as-Judge
Using a language model to score another model's output. Fast and scalable, but it has real failure modes such as self-preference and position bias, so the judge itself must be validated against human labels.
Grounding
Requiring a model to answer from provided evidence and to cite it, rather than from its own memory. The core defense against hallucination in a RAG system.
Embedding
A list of numbers that represents the meaning of a piece of text, so that similar meanings sit close together in vector space. The same model must embed both the corpus and the query.
ANN (Approximate Nearest Neighbor)
Search that finds vectors close to a query without scanning every item, trading a small chance of missing a true neighbor for a large gain in speed. HNSW and IVF are the common indexes.