Industrial Research World

Introduction Generative AI has become a shared C-Level priority with many enterprises setting goals in their annual statement and numerous press releases. As Generative AI is gaining traction, there is much anticipation around their evolving model performance capabilities. However, as developers increasingly move beyond Generative AI pilots, the trend is shifting to compound systems. The SOTA results often come from compound systems incorporating multiple components rather than relying solely on standalone models. A recent study by MIT Research has observed that 60% of LLM deployments in businesses incorporate some form of retrieval-augmented generation (RAG), with 30% utilizing multi-step chains or compound systems. Rise of Compound Systems A  Compound AI System  addresses AI tasks through multiple interconnected components, including several calls to different models, retrievers, or external tools. AI models are constantly improving, with scalability seemingly limitless. However, com

  Introduction: In the rapidly evolving landscape of Generative AI (Gen AI), managing the scale and cost of Large Language Models (LLMs) presents a formidable challenge for enterprises diversifying their application portfolios. As organizations increasingly integrate these powerful tools across various services, the absence of comprehensive visibility and cost controls can easily steer budgets into the red.  Karini AI  steps in as a game-changer, offering a meticulously designed dashboard that not only sheds light on the otherwise opaque realm of Gen AI expenditures but also puts the reins of cost management firmly in the hands of businesses. Exploring Karini’s Dashboards: Karini’s dashboards allow you to examine your cost, usage, and resource statistics thoroughly. They enable you to identify cost drivers, the most widely used resources, such as models and connectors, and overall statistics about data ingestion and deployment completions. It offers the following capabilities: Statisti

  When  creating a RAG (Retrieval Augmented Generation) system , you infuse a Large Language Model (LLM) with fresh, current knowledge. The goal is to make the LLM's responses to queries more factual and reduce instances that might produce incorrect or "hallucinated '' information. A RAG system is a sophisticated blend of generative AI's creativity and a search engine's precision. It operates through several critical components working harmoniously to deliver accurate and relevant responses. Retrieval:  This component acts first, scouring a vast database to find information that matches the query. It uses advanced algorithms to ensure the data it fetches is relevant and current. Augmentation:  This engine weaves the found data into the query following retrieval. This enriched context allows for more informed and precise responses. Generation:  This engine crafts the response with the context now broadened by external data. It relies on a powerful language model

  The advent of Generative AI has sparked a wave of enthusiasm among businesses eager to harness its potential for creating Chatbots, companions, and copilots designed to unlock insights from vast datasets. This journey often begins with the art of prompt engineering, which presents itself in various forms, including Single-shot, Few-shot, and Chain of Thought methodologies. Initially, companies tend to deploy internal chatbots to bolster employee productivity by facilitating access to critical insights. Furthermore, customer support, traditionally seen as a cost center, has become a focal point for optimization efforts, leading to the development of  Retrieval Augmented Generation (RAG) systems  intended to provide deeper insights. However, challenges such as potential inaccuracies or "hallucinations" in responses generated by these RAG systems can significantly impact customer service representatives' decision-making, potentially resulting in customer dissatisfaction. A