To speed up AI adoption and help citizens see the positive effects of AI sooner, there are a few tangible steps agencies can take.
While government agencies are understandably daunted at the prospect of reconfiguring their underlying data strategy, there are technology platforms that enable organisations to get more utility and insights out of the data they already have — no matter the location or format — without moving it or duplicating it. This “data mesh” approach can then serve as a unifying data layer that helps organisations most effectively integrate their own data with generative AI.
Further, many public sector organisations have been finding it beneficial to use retrieval augmented generation (RAG) workflows to connect their proprietary data with generative AI applications. This allows them to securely reap the benefits of generative AI technology without compromising the privacy of sensitive data. It essentially serves as a “context layer” that sends only the most relevant data to a generative AI application.
When implementing this context layer between private data and public generative AI, a vector database is a critical determinant of success. A vector database can normalise and streamline data so that you can search it quickly, no matter what format it’s in. Because a vector database stores information as vectors, or numerical representations of data, it’s much easier to find and correlate data across traditionally incompatible formats, such as text, images, sensor data, and more. Having the ability to quickly find what you need from all data then enables you to send the right, context-rich information to generative AI applications, thereby increasing the accuracy and relevance of AI outputs.
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