In the latest episode of Founded & Funded, Jon Turow speaks with Douwe Kiela, co-creator of Retrieval Augmented Generation (RAG) and co-founder of Contextual AI. They explore the evolution of RAG, its misconceptions, and its impact on enterprise AI. The discussion covers why RAG isn’t a one-size-fits-all solution, how to evaluate and scale generative AI ...
Recently, I faced a frustrating issue with my DJI LIDAR, which is crucial for my cinema camera’s autofocus and auto-follow features. When I attempted to get help through DJI’s AI support, the responses were unhelpful, leaving me to spend over an hour to finally receive a return label for repairs. This experience highlights a significant ...
In the fast-changing world of artificial intelligence, it’s essential to not just create smarter models but also to equip them with real-time knowledge and self-decision-making abilities. Retrieval-Augmented Generation (RAG) is a revolutionary method that enhances AI by combining generative models with current data from external sources. This approach improves accuracy, saves costs, and boosts transparency, ...
Creating engaging, on-brand content can be a challenge for Marketing teams when relying on traditional AI tools. They often produce generic, robotic outputs that fail to connect with audiences. Retrieval-Augmented Generation (RAG) offers a solution by allowing AI to reference your brand’s unique knowledge base, ensuring content aligns with your voice and messaging from the ...
Many Marketing teams struggle with AI-generated content that feels robotic and off-brand, leading to frustration. Traditional AI can create content quickly but often misses key details and consistency, while manual methods are slow. Retrieval-Augmented Generation (RAG) changes the game by allowing AI to access your brand’s specific knowledge base. This means it can produce content ...
This article critiques a standard prompt template used for RAG (Retrieval-Augmented Generation) task in AI question-answering systems. The author emphasizes that the prompt lacks essential features like examples, output formats, and edge case handling, which are crucial for achieving high accuracy in responses. Simply having good data and a solid framework isn’t enough; a well-structured ...
In today’s business world, large language models alone aren’t enough for effective AI applications. By integrating these models with enterprise data and using a technique called retrieval augmented generation (RAG), companies can create smarter AI agents that provide better insights. However, using structured data presents unique challenges compared to unstructured data. A new approach called ...
This tutorial series on AI agents begins with exploring their internal mechanics and the underlying technologies, including large language models (LLMs), function calling, and retrieval-augmented generation (RAGs). As AI agents gain momentum in the tech world, with industry leaders like Satya Nadella and Mark Zuckerberg recognizing their potential, the need to understand how to create ...
Contextual AI Inc. has launched its Contextual AI Platform, designed for creating specialized retrieval-augmented generation (RAG) agents that streamline knowledge work in businesses. This platform features enhanced technology, RAG 2.0, which allows AI models to access external data sources, improving their accuracy and usefulness. With all three necessary components for RAG systems—an LLM for responses, ...
This article discusses advancements in artificial intelligence, focusing on three key concepts: Retrieval-Augmented Generation (RAG), AI agents, and a new approach called Agentic RAG. RAG helps large language models access up-to-date information, overcoming the limitations of traditional models that often provide outdated answers. AI agents, on the other hand, are smart programs that make decisions ...
