Semantic Search
A search approach that uses AI to understand the meaning and intent behind queries rather than just matching keywords, delivering more relevant results by comprehending context and semantics.
Semantic search uses embeddings to represent queries and documents as numerical vectors, matching them by meaning rather than exact words. Google introduced BERT into search in 2019, affecting 10% of all English-language queries. The approach powers modern enterprise search, RAG systems, and e-commerce product discovery. Vector databases like Pinecone, Weaviate, and Milvus have raised over $300 million collectively to support the growing demand for semantic search infrastructure.
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Related Terms
Artificial General Intelligence (AGI)
A hypothetical form of AI that can understand, learn, and apply knowledge across any intellectual task at or above human level, rather than being specialized for specific tasks.
ChatGPT
OpenAI's conversational AI assistant, launched in November 2022, which catalyzed the current generative AI boom by demonstrating the capabilities of large language models to a mainstream audience.
Enterprise AI Adoption
The rate at which businesses integrate AI technologies into their operations, measured across functions like customer service, software development, marketing, and supply chain management.
Fine-Tuning
The process of further training a pre-trained AI model on a specific, smaller dataset to specialize it for a particular task or domain, requiring far less compute than training from scratch.
Foundation Model
A large AI model trained on broad data that can be adapted to a wide range of downstream tasks — examples include GPT-4, Claude, Gemini, and Llama.
Frontier Model
The most capable and advanced AI models at any given time, typically trained with the largest compute budgets and achieving state-of-the-art performance on benchmarks.
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