What is AI Search?
AI Search represents a fundamental shift from traditional keyword-based search to systems that understand meaning, context, and intent. Unlike conventional search engines that match query terms to document terms, AI search systems use machine learning models to comprehend the semantic meaning of both queries and content.
Modern AI search combines multiple technologies: vector embeddings that represent meaning as dense numerical vectors, neural networks that learn relevance patterns, and retrieval systems that can find conceptually related content even without exact keyword matches.
Key Technologies
Vector Search & Embeddings
Vector search uses embedding models to convert text, images, or other data into dense numerical representations (vectors) that capture semantic meaning. Similar concepts are mapped to nearby points in vector space, enabling similarity search that goes beyond keyword matching.
Modern embedding models like OpenAI's text-embedding-3, Cohere's embed-v3, and open-source models like BGE and E5 can produce embeddings with 768 to 4096 dimensions. These vectors are stored in specialized vector databases like Pinecone, Weaviate, Milvus, or Qdrant for efficient similarity search.
Vector embeddings are the foundation of modern AI search. They enable systems to find semantically related content without relying on exact keyword matches.
Neural Information Retrieval
Neural Information Retrieval (Neural IR) applies deep learning to the retrieval problem. Unlike traditional IR systems that use statistical methods like BM25, neural IR models learn relevance patterns from training data.
Key approaches include dense retrieval (using bi-encoders to independently encode queries and documents), cross-encoders (jointly processing query-document pairs for more accurate relevance scoring), and learned sparse retrieval methods like SPLADE that learn term importance weights.
Hybrid Search Systems
Hybrid search combines the strengths of different retrieval approaches. A typical hybrid system might use BM25 for lexical matching, dense vectors for semantic similarity, and re-ranking models for precision.
The combination strategy matters significantly. Common approaches include Reciprocal Rank Fusion (RRF), linear interpolation of scores, and learned combination weights. Modern search systems like those powering AI assistants typically use sophisticated hybrid approaches.
Semantic Understanding
Semantic understanding encompasses the AI system's ability to comprehend meaning beyond surface-level text. This includes understanding synonyms, hyponyms, entity relationships, contextual meaning, and even implied intent.
Transformer-based language models excel at semantic understanding because their attention mechanisms can capture long-range dependencies and contextual relationships. This enables AI search systems to understand that "best places to eat in London" and "top London restaurants" express similar intent.