Elevating AI: The Path to Efficient RAG Systems | #generative AI # RAG systems # GenAIOps platform # efficient response generation # data indexing # AI augmentation
The Rise of AI Agents: A Historical Perspective | #ai Agents Evolution # Generative AI # RAG Systems # Prompt Engineering # Chatbots Innovation
Generative AI: Pioneering Disruption in the Digital Age | #generative AI # Business Transformation # AI Innovation # Enterprise AI Solutions # Karini AI # AI in Marketing # AI in R&D # AI Tools for Business # Disruptive Technology # Future of AI
Generative AI has sparked a wave of excitement among businesses eager to create chatbots, companions, and co-pilots for extracting insights from their data. This journey begins with the art of prompt engineering, which includes various approaches like single-shot, few-shot, and chain of thoughts. Businesses often start by developing internal chatbots to help employees gain insights and boost their productivity. Given that customer support is a significant cost center, it has become a focus for optimization, with the development of Retrieval Augmented Generation (RAG) systems for enhanced insights. However, if a customer support RAG system provides inaccurate or misleading information, it could bias the judgment of representatives, leading to misplaced trust in computer-generated responses. Recent incidents involving entities like Air Canada and a Chevy chatbot have highlighted the reputational and financial risks of deploying unguided chatbots for self-service support. Imagine creating a financial advisor chatbot that offers human-like responses but is based on flawed or imaginative information, opposing sound human judgment.
https://www.karini.ai/blogs/bu....ilding-efficient-rag