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The Way to Perfect Retrieval Augmented Generation
In the age of AI-driven innovation, Retrieval-Augmented Generation (RAG) systems have emerged as transformative tools, seamlessly blending large language models (LLMs) with external knowledge retrieval. Yet, behind the seemingly effortless brilliance of these systems lies a story of relentless exploration, by these nine questions.
1. The Power of The Model Size:
Does the size matter? This question drove an exploration of how LLM parameters impact response quality. Larger models often dazzled with accuracy and context comprehension, yet they demanded immense resources.
The researchers investigated whether the size of models directly impacts the quality and accuracy of the generated responses. They found that Instruct45B outperforms Instruct7B. So the researchers believe that more size is more performance.
2. The Subtle Art of Prompting:
Crafting the perfect prompt became an art form capable of transforming ordinary responses into extraordinary insights.
