Bi-encoders encode queries and documents independently into embeddings, enabling fast vector similarity search, while cross-encoders jointly encode the query and document to compute a more accurate relevance score. In AI search, bi-encoders power scalable candidate retrieval; cross-encoders (neural rerankers) refine top candidates for quality.
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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