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This article was originally published on The New Stack on June 10, 2024. Retrieval augmented generation (RAG) has emerged as a leading pattern to combat hallucinations and other inaccuracies that affect large language model content generation. However, RAG needs the right data architecture around it to scale effectively and efficiently.

Despite the seemingly nonstop conversation surrounding AI, the data suggests that bringing AI into enterprises is still easier said than done. There’s so much potential and plenty of value to be captured — if you have the right models and tools. Implementing advanced AI today requires a solid data foundation as well as a set of solutions, each demanding its own tools and complex infrastructure.

In today’s data-driven world, businesses rely heavily on their dashboards to make informed decisions. However, traditional dashboards often lack the intuitive interface needed to truly harness the power of data. But what if you could simply talk to your data and get instant insights? In the latest version of Cloudera Data Visualization, we’re introducing a new AI visual that helps users leverage the power of Large Language Models (LLMs) to “talk” to their data..

If you’re not building with AI, are you even building these days? Sometimes, it seems not. AI has become such an integral part of workflows throughout many tools that a clear understanding of integrating it into your product and framework is critical. Django is such a framework that powers thousands of products across the web: Instagram, Pinterest, and Mozilla are all services built on Django.

Much has been said about how generative AI will impact the healthcare and life sciences industries. While generative AI will never replace a human healthcare provider, it is going a long way toward addressing key challenges and bottlenecks in the industry. And the effects are expected to be far-reaching across the sector.

In the world of natural language processing (NLP), the dream has always been to create systems that can understand, analyze, and generate human-like text. The good news is we are getting closer to this aspiration, thanks to innovations in artificial intelligence, particularly through platforms like OpenAI. In this article, we’ll explore text analysis with OpenAI, exploring the methodologies, tools, and metrics utilized to decipher the complex tapestry of human language. Let’s get started.

In the fast-evolving landscape of data science and machine learning, efficiency is not just desirable—it’s essential. Imagine a world where every data practitioner, from seasoned data scientists to budding developers, has an intelligent assistant at their fingertips. This assistant doesn’t just automate mundane tasks but understands the intricacies of your workflows, anticipates your needs, and dramatically enhances your productivity at every turn.