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      Kore.ai Technical Blog

      One-stop for everything related to AI-first
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      Cobus Greyling

      Cobus Greyling
      I explore and write about all things at the intersection of AI and language; Large Language Models, NLP, NLU, Chatbots, Voicebots & CCAI. Currently I’m an Evangelist @ Kore.ai
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      Recent Posts

      The Shifting Vocabulary of AI (Updated)

      The vocabulary in Generative AI and Conversational AI is evolving rapidly, introducing new terms that shape how we...

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      Can Minor Document Typos Comprehensively Disrupt RAG Retriever & Reader Components?

      Retrieval-Augmented Generation (RAG) is effective in leveraging LLM in-context learning (ICL) capabilities.
      But are we...

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      Data Design For Fine-Tuning To Improve Small Language Model Behaviour

      Teaching Small Language Models to Self-Correct & Reason by using creative data formats for fine-tuning data. Via Prompt...

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      Improve Conversational UIs Using Social Intelligence

      For Conversational User Interfaces to improve, the reliance on social intelligence will need to be leveraged. Many now...

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      Agentic Search-Augmented Factuality Evaluator (SAFE) For LLMs

      This study demonstrates that an LLM agent can outperform crowdsourced human annotators…

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      FaaF: Facts As A Function For Evaluating RAG

      There has been instances where another Language Model is used to vet the RAG output, which fails to detect incorrect...

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      Disambiguation: Dynamic Context For Effective RAG Question Suggestions

      This approach reminds of a technique initially implemented by IBM Watson called disambiguation. Where ambiguous input...

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      FIT-RAG

      FIT-RAG: Are RAG Architectures Settling On A Standardised Approach?

      As RAG is being used, vulnerabilities are emerging...

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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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