As the AI rage continues to seize industries by storm, so does the growing awareness of just how much it is going to play a pivotal role in our everyday lives. However, along with this new-found awareness comes questions—basic ones, like: are my outputs correct? How can I confirm I’m taking action based on the right intel?
One of the greatest criterions for accurate outputs is the diversification data inputs—which are often themselves, dispersed. Essentially, “what” and “how” much data are you feeding your AI beast before asking it to come up with an output?
Let’s address the “what” first. Developing robust and effective data curation and diversification habits are imperative to properly train your model—so it can rationalize real-world scenarios and create resiliency to unforeseen challenges, as well as prevent the models from producing bias outcomes. That’s where frameworks like retrieval-augmented generation (RAG), which guide how to optimize the output of a large language model (LLM) with more contextualized and up-to-date data—as well as where to get it, come into play.
Then we get to the “how.” Due to how far away data may be, unpredictable bandwidth, the sheer volume of data required—getting data to the AI models has historically been no easy feat. Today’s organizations will need to rely heavily on ultra-fast data access solutions to quickly and efficiently transfer the right data needed to properly train AI models.
Ready to equip your organization with the right guidelines—and tools—to make smarter, AI-based decisions? It all starts with AI anywhere, accessing data everywhere… in near real-time.
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