Author: Priyanka Vergadia

Three Paradigms of Retrieval-Augmented Generation (RAG) for LLMs

April 27, 2024 by Priyanka Vergadia

Retrieval-Augmented Generation (RAG) has emerged as a promising technique to enhance the capabilities of large language models by incorporating external knowledge sources. As research in this area has progressed, three distinct paradigms of RAG have emerged: Naive RAG, Advanced RAG, and Modular RAG. Naive RAG This represents the earliest and most basic RAG methodology. It […]

Boosting LLM Performance with the “More Agents” Method

March 25, 2024 by Priyanka Vergadia

Large language models (LLMs) like GPT-3 and ChatGPT have shown remarkable capabilities across many applications like language generation, understanding, reasoning, and coding. However, they can still struggle with very complex tasks. Researchers have explored different methods to enhance LLM performance, such as: Ensembling: Combining the outputs from multiple LLMs Multiple Agent Collaboration: Having different LLM […]

RAG vs Large Context Window LLMs: When to use which one?

March 24, 2024 by Priyanka Vergadia

Large language models (LLMs) are constantly evolving, and one key area of development is how much context they can consider when generating text. Large language models (LLMs) are incredibly powerful tools for processing and generating text. However, they inherently struggle to understand the broader context of information, especially when dealing with lengthy conversations or complex […]

The Secret Sauce of RAG: Vector Search and Embeddings

March 18, 2024 by Priyanka Vergadia

Retrieval-Augmented Generation (RAG) leverages the strengths of Large Language Models (LLMs) and external knowledge bases to deliver more informative and accurate outputs. Here's a breakdown of the key components focusing on data chunking, embeddings, vector databases, and their interaction

How to Make Your Generative AI More Factual

March 8, 2024 by Priyanka Vergadia

Large language models are powerful tools, but ensuring their accuracy is essential. Retrieval-Augmented Generation (RAG) emerges as a game-changer, bridging the gap between raw LLM potential and reliable, factual outputs. By harnessing the power of external knowledge bases, RAG empowers LLMs to deliver more informative, contextually relevant, and up-to-date responses across various industries. From personalized e-commerce experiences to enhanced medical diagnosis assistance, the applications of RAG are vast and hold immense promise for the future of Generative AI.

How to use Gemma models in Google Cloud?

February 22, 2024 by Priyanka Vergadia

Google just announced Gemma, a new family of lightweight and open-source AI models aimed specifically at developers and researchers like you. Here’s the lowdown: Understanding Gemma Gemma is a collection of lightweight AI models available in two sizes: 2B and 7B parameters. They’re built on the same research and technology as Google’s powerful Gemini models, […]

Zooming In: How Attention Makes LLMs Powerful

February 12, 2024 by Priyanka Vergadia

Imagine you’re at a bustling party, overwhelmed by conversations all around you. To make sense of it all, you naturally focus on specific voices – those relevant to your current topic or someone you find particularly interesting. This selective listening, the art of understanding by prioritizing, is at the heart of how LLMs process language. […]

LLM Parameters Explained

February 12, 2024 by Priyanka Vergadia

LLM Parameters Explained

Cloud Girl Bytes Aug 12, 2025

March 11, 2019 by Priyanka Vergadia

It all begins with an idea.

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