Retrieval-Augmented Generation (RAG) is a technique used by Large Language Models (LLMs) to improve the accuracy and relevance of their responses. Instead of relying solely on their pre-trained knowledge, RAG allows LLMs to retrieve information from external knowledge bases (like the internet) in real-time before generating an answer. This process helps provide up-to-date, factually grounded, and more comprehensive responses, overcoming the limitations of static training data.
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 are Embeddings in AI Search?
Embeddings are numerical representations of text, images, or other data that capture their semantic meaning and relation...
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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