Leveraging genAI to reduce redundant data in relational databases

With the widespread adoption of generative AI, textual content is increasingly being generated and stored in databases across various applications. From customer service responses to personalised content recommendations, AI-driven text generation is transforming how we interact with data.

As a working example, let's take a customer service chatbot that retrieves common responses from a relational database, which have previously been generated by an LLM, to help customers with various issues.

Let's start by defining 'similarity' of responses in a quantifiable way. This can be done by transforming the response text into an embedding, which is its numerical vector representation, by leveraging an embedding model.

With an increasing number of AI-powered digital products in our portfolio, we have frequently encountered the challenge of managing and maintaining database performance amidst the surge of generated text.