AI tip of the month: AI & RAG for really smart bots

RAG stands for "Retrieval-Augmented Generation" and combines two technologies:

1. Information Retrieval: The system searches for relevant information from an internal or external knowledge database.

2. Generative AI: The AI uses this information to generate comprehensible and context-related answers.

This allows a chatbot to respond not only with general knowledge, but also with specific, up-to-date and company-internal information.

Why RAG is important for companies

Without RAG, AI models are often limited to static knowledge that was current at the time they were trained. However, there is constantly new information in companies, for example about products, guidelines or processes. A RAG-supported chatbot can:

• Help employees by always accessing the latest internal documents.

• Improve customer support by accessing the latest FAQs, manuals and guidelines.

• Ensure compliance by referring to relevant regulations and company policies.

The perfect combination: AI + RAG

A RAG-supported chatbot can not only answer general queries, but also use internal company knowledge. This is particularly important for use in large companies, as

• knowledge from different departments can be integrated,

• the bot reacts specifically to company specifics and

• customer support and internal processes can be automated and personalized.

Only with RAG does AI become really smart. Instead of just reproducing pre-trained knowledge, AI can access company-specific and up-to-date information in a targeted manner. This results in powerful, precise and secure chat and voicebots that make companies more efficient and improve support.