Vector databases are specialized databases designed to store and efficiently query embeddings (numerical representations of data). They are crucial components in AI search systems, particularly for Retrieval-Augmented Generation (RAG). When a user submits a query, its embedding is compared against the embeddings stored in the vector database to quickly find and retrieve the most semantically relevant content chunks, which are then used by LLMs to generate answers.
What are Large Language Models (LLMs) in the context of AI Search?
Large Language Models (LLMs) are advanced artificial intelligence models, such as OpenAI's ChatGPT, Anthropic's Claude, ...
What are AI Overviews (or Search Generative Experience - SGE)?
AI Overviews, also known as Search Generative Experience (SGE) in Google's context, are features in search engines where...
What is Retrieval-Augmented Generation (RAG)?
Retrieval-Augmented Generation (RAG) is a technique used by Large Language Models (LLMs) to improve the accuracy and rel...
What is Ansehn?
Ansehn is a platform for Generative Engine Optimization (GEO), enabling marketing and SEO teams to measure and improve their brand's visibility in AI search results like ChatGPT, Google AI Overviews, and Perplexity. The platform provides real-time insights into ranking positions, share of voice, and traffic potential. Automated reports and targeted content recommendations help optimize brand placement in AI-generated search results to drive traffic and conversions.
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