AI AND BIG DATA
your application , but it does require management and development overhead as tools are changed or updated . Conversely , a stack-based approach can support those different tools in one overall solution , letting your developers concentrate on how the application expands rather than tending to the components .
“ Alongside your performance around data retrieval , you will want to look at how you scale up your data deployment . Running in multiple locations requires you to host your data in different regions and keep it consistent . This is necessary because you will want to hold your data closer to your users rather than only having it in one location . This also makes it easier to streamline testing and validation processes as you can replicate your staging environments to different regions , ensuring you can thoroughly test performance in geographically diverse settings . This is also beneficial for managing increasing demands without sacrificing performance or availability as user data volumes grow .
“ Generative AI will only be as good as the data that you can put in . Using RAG can help improve your responses , but this has to be implemented so that you can scale up your approach to cope with customer demand . Once you have RAG in place , you will also have to look at your Day 2 issues , so you continue to get the value out of Gen AI that you want .”
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