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Save Big on AI Costs: How RAG + Edge Computing Supercharge Your Efficiency!🚀💻
Unlock the Secret to Cutting Costs and Boosting Performance with RAG + Edge Computing 🚀💸
In the world of AI-driven applications, Retrieval Augmented Generation (RAG) is all the rage for improving the efficiency of large language models (LLMs). 🚀 But here’s the thing: while RAG apps already save businesses up to 60% in costs compared to standard LLMs, what if I told you there’s a way to save even more? 😱💰
In today’s post, we’re going to dive deep into RAG apps — the challenges, the solutions, how they work, and how you can squeeze even more savings out of them. Ready to save some serious cash and supercharge your AI applications? Let’s go! 🔥
1. The Current Challenges of RAG Apps 🔍
While RAG apps are a game-changer, they come with their own set of challenges that can limit their full potential:
a. High Latency 🐢
RAG apps often involve retrieving data from external databases before generating responses. This retrieval step can introduce latency, especially if the data is large or complex. As a result, users may experience slower response times…
