Flat files have long been fundamental in the world of data management, providing a simple yet powerful means of storing, exchanging, and analyzing data. They are favored for their ease of use, human readability, and wide compatibility with various software applications.

In the rapidly evolving landscape of artificial intelligence, generative models have become a cornerstone, powering applications from creative content generation to sophisticated conversational agents. One of the innovative advancements in this domain is Retrieval-Augmented Generation (RAG), a hybrid approach that combines the strengths of retrieval-based and generative models to enhance the quality and relevance of generated content.

In the constantly changing environment of artificial intelligence, one of the most significant advancements in recent years has been the development of Large Language Models (LLMs). These models, powered by deep learning techniques and huge data collections, have revolutionized the way we interact with technology, offering unique capabilities in natural language understanding and generation.

Artificial Intelligence (AI) has swiftly become a fundamental part of our daily lives, transforming industries from healthcare to finance, entertainment to transportation. However, at the heart of these complex systems lies the essence of defining the functionality and performance of AI models. In this article, we will explore what AI parameters are, why they matter, and how they shape the capabilities of AI systems.

Hallucinations in AI present a wonderful but still potentially alarming aspect of artificial intelligence research and development. Much like in human cognition, AI systems can sometimes generate outputs that are not true in reality, leading to what could be described as "hallucinations." These hallucinations can manifest in various forms, ranging from misinterpretations of input data to entirely fabricated outputs.