Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for grounding LLMs with external knowledge, but it comes with its own set of challenges, especially at enterprise scale. When dealing with millions of documents, naive implementa...
In this blog, we will discuss how Vector Embeddings work in capturing semantic meaning and relationships. These multi-dimensional numerical vectors are then used by machine learning algorithms to perform tasks such as similarity searches, clustering,...
Have you ever wondered how computers understand things like words, pictures, or even your favorite songs? You might think computers just “see” words like we do, but actually, they only understand numbers , a lot of numbers! To make sense of complex t...
In today’s information-heavy world, professionals like lawyers, researchers, and analysts often deal with hundreds of pages of PDFs. Finding precise answers manually is time-consuming, and asking a standard LLM to summarize or answer can result in ge...
Retrieval-Augmented Generation (RAG) has become one of the most powerful techniques in the world of large language models (LLMs). At its core, RAG is simple: instead of relying only on the LLM’s memory, we fetch relevant knowledge from an external so...
Retrieval-Augmented Generation (RAG) has revolutionized how AI systems access and utilize external knowledge. By combining document retrieval with text generation, RAG promises more accurate, up-to-date responses while reducing hallucinations. Howeve...
Large Language Models (LLMs) like ChatGPT, Claude, or Gemini are powerful, but they come with a major limitation: their knowledge is frozen at the point of training. Ask a model trained in 2023 about a 2025 news headline, and it might hallucinate, ma...
Retrieval-Augmented Generation (RAG) is the backbone of modern AI systems that need to reason over private or domain-specific data. Real-world RAG pipelines often crumble under messy user input, ambiguous queries, or irrelevant retrievals. The classi...
Introduction Retrieval-Augmented Generation (RAG) is a cutting-edge AI architecture that combines the power of large language models (LLMs) with external knowledge retrieval. RAG systems are designed to generate more accurate, up-to-date, and context...
Heyy! So, let’s break down Retrieval Augmented Generation (RAG). It’s basically a method that mixes retrieval systems with generative models, like those large language models (LLMs) we hear about. This combo really boosts the accuracy and relevance o...
