Imagine you’re writing a long-form article, and you need to weave in information from a vast database of knowledge. You’d need to remember the context of your writing, the specific information you’ve already used, and how new information fits into…
The world of AI is buzzing with excitement about Retrieval-Augmented Generation (RAG) – a powerful technique that combines the best of both worlds: large language models (LLMs) and external knowledge bases. RAG empowers LLMs to access and leverage information from…
The advent of large language models (LLMs) has revolutionized various industries, and the recent introduction of Gemini PDF feature by Google AI promises to further disrupt the landscape of document processing. This groundbreaking feature leverages the power of LLMs to…
In the realm of artificial intelligence (AI) and machine learning (ML), embeddings play a pivotal role. They are essentially numerical representations of complex objects, such as words, images, or videos, that capture their essence and relationships with other objects. Embeddings…
Retrieval Augmented Generation (RAG) has emerged as a powerful technique for enhancing the capabilities of Large Language Models (LLMs). By combining the strengths of information retrieval and generative AI, RAG systems can access and process vast amounts of information to…