RAG & Retrieval
Hallucination
When a model generates confident-sounding information that is factually incorrect or fabricated.
In depth
When a model generates confident-sounding information that is factually incorrect or fabricated. Hallucinations happen because LLMs predict plausible-sounding text, not verified facts. Techniques like RAG, grounding, and structured output help reduce but do not eliminate hallucinations.
Example
Techniques like RAG, grounding, and structured output help reduce but do not eliminate hallucinations.
Go deeper at Developers Digest
Hands-on guides, comparisons, and tutorials that cover RAG & Retrieval.
FAQ
What is Hallucination?
When a model generates confident-sounding information that is factually incorrect or fabricated.
Why does Hallucination matter for AI developers?
Hallucination sits in the RAG & Retrieval part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Hallucination?
Developers Digest publishes tutorials and videos that cover RAG & Retrieval topics including Hallucination. Check the blog and YouTube channel for hands-on walkthroughs.
Related terms
RAG & Retrieval
Grounding
Connecting a model's responses to verified, external data sources rather than relying solely on its training data.
RAG & Retrieval
Knowledge Cutoff
The date after which a model has no training data.
RAG & Retrieval
Hybrid Search
A retrieval strategy that combines keyword-based search (BM25, TF-IDF) with semantic vector search (embeddings) to get the best of both approaches.
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