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The momentum of AI-drivenapplicationsis accelerating around the world and shows little sign of slowing.
Faced with new challenges of deploying the technology at scale.
More and more enterprise workflows are embedding calls to these AI Models are dramatically increasing usage.
Do the use cases justify the escalating spending on the latest models?
How can businesses navigate the rising costs ofdatawhile powering the AI applications they need?
VP of AI & Search Products at Redis.
Secondly, in many cases organizations want to customize their AI models by fine-tuning them.
It refers to storing and reusing the results of previous computations based on their semantic meaning.
This approach helps reduce redundant computations and improves efficiency in applications like inference or search.
This method is crucial for building scalable and responsive generative AI applications orchatbots.
This approach not only optimizes cost but also accelerates response times, helping businesses achieve more with less investment.
This can help them strike that crucial balance between performance and cost.
Techniques such as semantic caching can play a vital role in this.
As generative AI systems get more and more complex, everyLLMcall needs to be as efficient as possible.
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The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc.
