Category Archives: Google

Large Language Models (LLM): A Comprehensive Overview

IntroductionLarge Language Models (LLMs) have revolutionized the field of Natural Language Processing (NLP), demonstrating an unprecedented ability to understand and generate human-like text. These models are trained on vast amounts of data, learning statistical relationships from text documents during a computationally intensive self-supervised and semi-supervised training process. What is a Large Language Model (LLM)?An LLM […]

Retrieval-Augmented Generation (RAG): A Deep Dive

IntroductionRetrieval-Augmented Generation, commonly known as RAG, has been making waves in the realm of Natural Language Processing (NLP). At its core, RAG is a hybrid framework that integrates retrieval models and generative models to produce text that is not only contextually accurate but also information-rich. What is RAG?Retrieval-Augmented Generation (RAG) is the process of optimizing […]

Google Gemini AI: The Next Generation of Artificial Intelligence

IntroductionArtificial Intelligence (AI) has been the focus of many research colleagues and has been the driving force behind many technological advancements. The transition we are seeing right now with AI is believed to be the most profound in our lifetimes, far bigger than the shift to mobile or to the web before it. AI has […]

Unveiling Generative AI: An Approachable Guide

In the realm of artificial intelligence, one of the most intriguing advancements is Generative AI. But what exactly is Generative AI, and why is it causing such a buzz? At its essence, Generative AI is a branch of artificial intelligence that focuses on creating something new—whether it’s images, music, text, or even entire scenarios—based on […]

LangChain: A Framework for Building Applications with Large Language Models

Large language models (LLMs) are neural network models that can generate natural language texts based on some input, such as a prompt, a query, or a context. LLMs have shown impressive results in various natural language processing tasks, such as text summarization, machine translation, question answering, and code generation. However, building applications that use LLMs […]