AI Fusion New RAG Technique for Improved Accuracy and Cost Savings: Interactive RAG #rag #llm
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In this video, we’re diving into an exciting new approach to Retrieval-Augmented Generation, or RAG—introducing Interactive RAG. If you’ve been using traditional RAG, you already know how it can improve AI responses by pulling relevant information from external sources. However, traditional RAG has limitations when it comes to cost-efficiency and precision. That’s where Interactive RAG comes in.
Interactive RAG empowers you with more control by adding an interactive layer to the retrieval process. Instead of automatically sending every retrieved chunk to the AI model, Interactive RAG lets you decide which chunks to include. This simple but powerful change allows you to reduce token usage, lower costs, and fine-tune the AI’s responses to be exactly what you need—focused, accurate, and highly relevant.
In this video, we’ll start with a quick overview of how traditional RAG works, and then I’ll walk you through the unique benefits of Interactive RAG. We’ll even look at an example, where we interact with retrieved data about medications to get a precise response. This innovation is perfect for anyone looking to maximize the accuracy of AI outputs while keeping costs under control.
If you’re passionate about getting the most out of AI and want to stay updated on cutting-edge advancements like Interactive RAG, make sure to hit that subscribe button and turn on notifications!
#InteractiveRAG #RetrievalAugmentedGeneration #RAG #AIInnovation #CostEfficiency #PrecisionAI #ArtificialIntelligence #GenerativeAI #MachineLearning #DataControl #AIDevelopment #TokenOptimization #AItools #AIResearch #AICommunity #FutureOfAI #SmartAI #ContentCreation
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