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How RAGs Help Mitigate LLM Hallucinations: 5 Use Cases

How RAGs Help Mitigate LLM Hallucinations: 5 Use Cases

RAG constitutes one of the most exciting developments of artificial intelligence. The technique allows combining Large Language Models (LLMs) with external knowledge bases in order to increase the accuracy and reliability of generated answers. In other words, it means grounding the generative AI model with facts and information that were not previously employed to train the model.

How RAGs Help Mitigate LLM Hallucinations: 5 Use Cases

RAG constitutes one of the most exciting developments of artificial intelligence. The technique allows combining Large Language Models (LLMs) with external knowledge bases in order to increase the accuracy and reliability of generated answers. In other words, it means grounding the generative AI model with facts and information that were not previously employed to train the model.

How MLOps accelerates AI Model Deployment

How MLOps accelerates AI Model Deployment

MLOps is the bridge between machine learning and operations. A combination of methodology, tools and processes, it streamlines and automatizes the ML model lifecycle management, integrating ML workflows, pipeline and automation, continuous delivery and observability. This article explains how MLOps can be the conductor that makes all the difference.

Why do we need MLOps?

Why do we need MLOps?

MLOps and AI infrastructures are topics that have been widely discussed in recent months, even more so after the rise of technologies around LLMs like ChatGPT. In this blog post, we’re going to give a short and gentle introduction to these concepts by introducing their basic aspects.