Semantic Search
A search technique that understands the meaning behind queries rather than just matching keywords.
Semantic search uses vector embeddings to find documents based on conceptual similarity. A query about "employee termination procedures" will match documents discussing "offboarding processes" even if the exact words never appear together.
This approach is transformative for document intelligence because business users rarely know the exact phrasing used in contracts or reports. Semantic search bridges the vocabulary gap between how users ask questions and how documents are written.
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.
Hallucination
When an AI model generates plausible-sounding but factually incorrect or fabricated information.
Large Language Model (LLM)
A neural network trained on massive text corpora that can understand and generate human language.
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