A technique that enhances LLM responses by retrieving relevant current information from external sources.
Retrieval Augmented Generation (RAG) is a technique that combines LLM capabilities with real-time information retrieval. Instead of relying solely on training data, RAG systems search external sources (like the web) to find relevant, current information, then use this to generate more accurate, up-to-date responses. Perplexity is a prominent example of RAG in action. RAG is increasingly important for AI visibility because it allows AI assistants to access information published after their knowledge cutoff.
We optimize your digital presence for both LLM training data and RAG retrieval systems, ensuring comprehensive AI visibility.
RAG systems can surface current information about your brand, overcoming training data limitations. Optimizing for RAG means ensuring your content is accessible and well-structured for retrieval systems.
Perplexity searching the web to answer current questions
ChatGPT's browse feature retrieving recent information
Claude using provided documents to enhance responses
Perplexity is built on RAG. ChatGPT and Claude have optional browsing capabilities. Most major assistants are adding RAG features.
Ensure your website is crawlable, content is well-structured, and information is clearly presented. RAG systems need to quickly find and extract relevant information.
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