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      Kore.ai Technical Blog

      One-stop for everything related to AI-first
      experience automation

      T-RAG = RAG + Fine-Tuning + Entity Detection

      The T-RAG approach is premised on combining RAG architecture with an open-source fine-tuned LLM and an entities tree...

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      Beyond Chain-of-Thought LLM Reasoning

      This approach can be implemented on a prompt level and does not require any dedicated frameworks or pre-processing.

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      Comparing Human, LLM & LLM-RAG Responses

      A fairly recent study, focused on the healthcare & preoperative medicine compared expert human feedback with LLM...

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      Craft Successful Conversational User Interfaces: Align User Intent With Developed Intent

      In this article I illustrate how to achieve intent alignment by making use of the Kore.ai XO Platform Intent Discovery...

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      A Benchmark for Verifying Chain-Of-Thought

      A Chain-of-Thought is only as strong as its weakest link; a recent study from Google Research created a benchmark for...

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      Seven RAG Engineering Failure Points

      Retrieval-Augmented Generation (RAG) systems remains a compelling solution to the challenge of relevant up-to-date...

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      UniMS-RAG: Unified Multi-Source RAG for Personalised Dialogue

      This study explores how the RAG process can be decomposed, adding elements like multi-document retrieval, memory and...

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      Chain-of-Symbol Prompting (CoS) For Large Language Models

      LLMs need to understand a virtual spatial environment described through natural language while planning & achieving...

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      Concise Chain-of-Thought (CCoT) Prompting

      Traditional CoT comes at a cost of increased output token usage, CCoT prompting is a prompt-engineering technique which...

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      Understanding LLM User Experience & Expectation

      This study surfaces valuable insights into the frequency of LLM use together with user intents, expectations and...

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