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      Kore.ai Technical Blog

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      Active Prompting with Chain-of-Thought for Large Language Models

      By using AI accelerated human annotation this framework removes uncertainty and introduces reliability via a...

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      Teaching LLMs To Say, “I don’t know”

      Instead of stating that it does not know, LLMs hallucinate. Hallucination can best be described as highly plausible,...

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      Improving Text Embeddings with LLM Generated Synthetic Data

      Value Discovered

      Text embeddings are really playing a pivotal role in retrieving semantically similar text for RAG...

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      LLM Performance Over Time & LLM Task Contamination

      A recent study revealed that LLMs perform significantly better on datasets released before their training data creation...

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      Data Design For Fine-Tuning LLM Long Context Windows

      Fine-Tune your LLM To fully utilise the available context window.

      Introduction

      Something I find intriguing...

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      Five Levels Of AI Agents

      Introduction

      This is a topic I really enjoyed researching and I was looking forward to writing this. Mostly because I...

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      Comparing LLM Agents to Chains: Differences, Advantages & Disadvantages

      In this article I put into plain terms the difference between chains and agents, and what will work best for certain...

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      Exploring the Purpose, Power & Potential of Small Language Models (SLMs)

      Introduction

      A multitude of Small Language Models (SLMs) are readily available as open-source resources, easily...

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      LLMs Training SLMs

      Introduction

      The primary focus in training Orca-2, was to create an open-sourced Small Language Model (SLM) which...

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

      Introduction

      Retrieval-augmented generation (RAG) is widely adopted in Generative AI applications for several reasons.

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