Generative AI should help you differentiate what your company does. However, using public LLMs alone will not deliver this, and you will sound the same as everyone else. Companies can make their generative AI strategies more effective and tailored for them and for employees by bringing their own data to the table using retrieval augmented generation, or RAG.
RAG takes your own data, gets it ready for use philippines whatsapp number data with generative AI, and then passes this data as context into the LLM when your employee asks for a response. RAG is part of solving problems like hallucinations, and it also makes results more relevant for your organization and your customers, rather than getting similar results to other companies that are asking for the same kinds of questions. This is something that you have to do for your organization and customers, as no other company will have the same depth or combination of data that you can provide.
To implement this, you will have to combine various tools from vector data stores and AI integrations to build a RAG stack that makes it easier and faster to get started. Delivering this quickly will help you prevent some of those “off the books” deployments that teams might try to do for themselves while they wait for central IT. Techniques like RAG also reduce the risks of data leaks by allowing you to leverage company data for improved context without training it into the LLM.