Tag: RAG

Prologue As the wave of interest in Large Language Models (LLMs) surges, many developers and organisations are busy building applications harnessing their power. However, when the pre-trained LLMs out of the box don’t perform as expected or hoped, the question on how to improve the performa...

In the ever-evolving world of machine learning, choosing the right tool can sometimes feel like finding a needle in a haystack. Today, we’re diving deep into two popular approaches when working with large language models like GPT-4: RAG (Retrieval-Augmented Generation) and fine-tuning. Grab a ...

As the wave of interest in Large Language Models (LLMs) surges, many developers and organisations are busy building applications harnessing their power. However, when the pre-trained LLMs out of the box don’t perform as expected or hoped, the question on how to improve the performance of the L...

In the ever-evolving world of machine learning, choosing the right tool can sometimes feel like finding a needle in a haystack. Today, we’re diving deep into two popular approaches when working with large language models like GPT-4: RAG (Retrieval-Augmented Generation) and fine-tuning. Grab a ...

Choosing between Retrieval-Augmented Generation (RAG) and fine-tuning is crucial for building effective large language model applications. Though both techniques boost model capabilities, they optimize different objectives under varying constraints. Fine-tuning adapts the entire model throug...