Hallucination
When an AI model generates plausible-sounding but factually incorrect or fabricated information.
Hallucinations occur because language models predict statistically likely text rather than verified facts. The model may invent citations, misstate numbers, or confidently assert claims that have no basis in the source material.
In regulated industries, hallucinations are not just annoying — they are dangerous. RAG-based architectures with citation verification significantly reduce hallucination risk by constraining the model to answer only from retrieved source documents.
More ai/ml Terms
Retrieval-Augmented Generation (RAG)
An AI architecture that combines information retrieval with text generation to produce answers grounded in source documents.
Vector Embedding
A numerical representation of text as a high-dimensional vector, enabling semantic similarity comparisons between passages.
BM25
A probabilistic keyword-ranking algorithm that scores documents by term frequency and inverse document frequency.
Chunking
The process of splitting large documents into smaller, overlapping segments optimized for retrieval and embedding.
Large Language Model (LLM)
A neural network trained on massive text corpora that can understand and generate human language.
Fine-Tuning
The process of further training a pre-trained model on domain-specific data to improve its performance on targeted tasks.
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