RAG Search.
Ask questions and get answers about netnode.ch. The Search Engine is powered by Retrieval Augmented Generation (RAG) and provides accurate and relevant results.
Demo
How it works
Retrieval-Augmented Generation
A Retrieval-Augmented Generation (RAG) search combines a retrieval component with a generative AI model to deliver contextually precise and informative answers. Essentially, a RAG search accesses a knowledge base before the AI generates text, allowing the model to utilize up-to-date or specific information without requiring that information to be embedded within the model itself.
Intuitive
A RAG is more intuitive to use than a traditional site search—it feels like chatting with the website. The interaction is more natural, allowing users to reach the desired results quickly. Additionally, as a trust-building element, each source used by the AI to compile answers is displayed.
Use Case
RAG (Retrieval-Augmented Generation) is particularly useful in scenarios that require precise, context-rich, and up-to-date information. Here are some typical use cases:
- Customer Support and Chatbots: for precise, context-aware responses.
- Research and Reporting: to efficiently generate fact-based reports.
- Science and Medicine: to provide support with current scientific data.
- Technical Support and Documentation: for quick, accurate answers to technical questions.
- E-Learning and Knowledge Management: to dynamically create learning content.
- Product and Market Analysis: for insights on market trends and competitors.
RAG enhances information availability and accuracy across these areas.
