Another revolutionary aspect of AI in Digital Workplaces is its ability to respond to user queries naturally in specific and business contexts. Thanks to AI engines such as RAG (Retriever, Answerer, Generator), AI can query complex document bases and provide relevant answers, helping to solve information overload and speed up information searches .
Here too, the use of this technology is transparent for the user and consists of several steps:
An artificial intelligence engine, such as " embedding ", is used to cut up the south africa telegram data documents and store them in a vector database.
User search goes through the same translation path in embedding and storage as in vector database
The main AI engine of the “ LLM ” (“large language model”) type is queried with the two inputs: the documentation on which the search is carried out + the user research
This RAG mechanism must be supplemented by “ pre-prompting ” work in order to offer an optimal employee experience.
Some good practices should be applied.