Use your proprietary data along with the prompt to improve the results, reduce hallucinations, increase accuracy, etc. But many organizations can now discover if you do this with a disconnected, disjointed data environment, GenAI as-is with no consideration for the data. Not only might this not solve the problem but now you’re presented with new problems. The GenAI or LLM is not “seeing” your proprietary data with the proper controls. It is feeding data that might ultimately not be important to the query. Or it is hong kong whatsapp number data struggling to find the correct data that relates to the prompt.
AI and Data First
This is why you need to take an AI-and-data-first view when it comes to these solutions. Data is not only the foundation of AI, but it helps to create the scaffolding for it as well. It should provide the AI with the context for your organization. It should present a model of the world your organization exists in. Frame the prompt and the desired response with the data that is most important to the question being asked – all the while governing the data that the AI has access to and monitoring the response so that there is no data leakage or responses that contravene your internal controls and external regulations and standards.
When combined, these literacies create a synergistic effect. Data literacy ideally means that the data feeding AI systems is accurate, well-governed, and contextually relevant, forming a solid foundation for AI applications. Meanwhile, AI literacy allows employees to maximize the value derived from this data, using AI to uncover insights that might otherwise remain hidden. Together, they enable organizations to develop more robust, innovative solutions that drive growth and transformation.