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      Kore.ai Technical Blog

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      Challenges In Adopting Retrieval-Augmented Generation Solutions

      I have thoroughly examined some of the recent academic papers on RAG (Retrieval-Augmented Generation) and have...

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      Adaptive-RAG

      It is evident that there needs to be a balance between query time, quality in terms of performance, but also efficiency.

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      Large Language Models Excel At In-Context Learning (ICL)

      Studies have shown that, when supplied with a contextual reference at Inference, LLMs opt to make use of the contextual...

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      Retrieval Augmented Fine-Tuning (RAFT)

      Adapting Language Model to Domain Specific RAG...

      Using RAFT, when presented with a question and a batch of retrieved...

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      DRAGIN: Dynamic RAG Based On Real-Time Information Needs Of LLMs

      A study introduced a novel approach to RAG but more importantly the study highlighted a number of shortcomings of...

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      A Study Comparing RAG & Fine-Tuning For Knowledge Base Use-Cases

      The selection of technology should be driven primarily by the requirements and goals of a particular use-case or...

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      RAT — Retrieval Augmented Thoughts

      Let me first start with a few general observations…

      There is a tension between achieving efficiency within...

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      A Short History Of RAG

      One of the most popular themes currently around Large Language Models is the idea of Retrieval Augmented Generation...

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      Chain-of-Instructions (CoI) Fine-Tuning

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

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      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...

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