Metrics are a cornerstone element in evaluating any AI system, and in the case of large language models (LLMs), this is no exception. This article demystifies how some popular metrics for evaluating language tasks performed by LLMs work from inside, supported by Python code examples that illustrate how to leverage them with Hugging Face libraries […]
Understanding what’s happening behind large language models (LLMs) is essential in today’s machine learning landscape. These models shape everything from search engines to customer service, and knowing their basics can unlock a world of opportunities. This is why we are going to break down some of the most important concepts behind LLMs in a very […]
I believe in the ‘learning by doing’ approach—you learn more this way. However, as a beginner, you need to be careful not to overwhelm yourself by jumping into a complex project too soon. To help you get comfortable working with LLMs and RAG, I’ll be sharing 5 different projects that are perfect for beginners. The […]
Graph RAG, Graph RAG, Graph RAG! This term has become the talk of the town, and you might have come across it as well. But what exactly is Graph RAG, and what has made it so popular? In this article, we’ll explore the concept behind Graph RAG, why it’s needed, and, as a bonus, we’ll […]
Natural language generation (NLG) is an enthralling area of artificial intelligence (AI), or more specifically of natural language processing (NLP), aimed at enabling machines to produce human-like text that drives human-machine communication for problem-solving. This article explores what NLG is, how it works, and how this area has evolved over recent years while underscoring its […]
Check out the previous articles in this series: Understanding RAG Part I: Why It’s Needed Understanding RAG Part II: How Classic RAG Works Having previously introduced what is RAG, why it matters in the context of Large Language Models (LLMs), and what does a classic retriever-generator system for RAG look like, the third post in […]
In the first post in this series, we introduced retrieval augmented generation (RAG), explaining that it became necessary to expand the capabilities of conventional large language models (LLMs). We also briefly outlining what is the key idea underpinning RAG: retrieving contextually relevant information from external knowledge bases to ensure that LLMs produce accurate and up-to-date […]
Natural language processing (NLP) is an area of artificial intelligence (AI) aimed at teaching computers to understand written and verbal human language and interact with humans by using such a language. Whilst traditional NLP methods have been studied for decades, the recent emergence of large language models (LLMs) has virtually taken over all developments in […]
