With Progress Agentic RAG, you can index appropriate data to provide an LLM more relevant, accurate information. And you can do this in a no-code interface. Let’s explore the dashboard together!
Embeddings translate text into numerical vectors to plot semantic meaning. This allows RAG systems to find semantically similar concepts even when the words are different.
Rather than relying exclusively on general knowledge available to an LLM, RAG allows you to connect the model to specific reference sources, so your AI can provide more accurate, relevant information.