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DRAGIN: Dynamic RAG Based On Real-Time Information Needs Of LLMs A Recent study introduced a new approach to RAG but more importantly the study highlighted a number of shortcomings of RAG as we know it.

A New Study Compares RAG & Fine-Tuning For Knowledge Base Use-Cases This study illustrates again that the use-case informs & dictates the technology.

Revolutionize lead capture with Kore.ai's AI chatbot in HubSpot. Automate collection, enhance segmentation, and gain real-time insights to boost sales.

Boost LLM reasoning with RAT — a smart blend of retrieval + chain-of-thought to improve accuracy.

This approach draws inspiration from Chain-of-Thought (CoT) prompting which generates step-by-step rationales from LLMs.

Performing Multiple LLM Calls & Voting On The Best Result Are Subject To Scaling Laws More LLM calls enhance performance on easy queries but diminish it on hard ones. So what scaling laws can be discovered & leveraged? What is the optimal number of LLM Calls?