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In: Artificial Intelligence

Generative AI question-answering applications are pushing the boundaries of enterprise productivity. These assistants can be powered by various backend architectures including Retrieval Augmented Generation (RAG), agentic workflows, fine-tuned large language models (LLMs), or a combination of these techniques. However, building and deploying trustworthy AI assistants requires a robust ground truthContinue Reading

In: Artificial Intelligence

This post was co-authored with Johann Wildgruber, Dr. Jens Kohl, Thilo Bindel, and Luisa-Sophie Gloger from BMW Group. The BMW Group—headquartered in Munich, Germany—is a vehicle manufacturer with more than 154,000 employees, and 30 production and assembly facilities worldwide as well as research and development locations across 17 countries. Today, theContinue Reading

In: Artificial Intelligence

Time series forecasting is critical for decision-making across industries. From predicting traffic flow to sales forecasting, accurate predictions enable organizations to make informed decisions, mitigate risks, and allocate resources efficiently. However, traditional machine learning approaches often require extensive data-specific tuning and model customization, resulting in lengthy and resource-heavy development. EnterContinue Reading

In: Artificial Intelligence

Building cloud infrastructure based on proven best practices promotes security, reliability and cost efficiency. To achieve these goals, the AWS Well-Architected Framework provides comprehensive guidance for building and improving cloud architectures. As systems scale, conducting thorough AWS Well-Architected Framework Reviews (WAFRs) becomes even more crucial, offering deeper insights and strategicContinue Reading

In: Artificial Intelligence

Amazon Bedrock Knowledge Bases offers a fully managed Retrieval Augmented Generation (RAG) feature that connects large language models (LLMs) to internal data sources. It’s a cost-effective approach to improving LLM output so it remains relevant, accurate, and useful in various contexts. It also provides developers with greater control over theContinue Reading

In: Artificial Intelligence

Increasingly, organizations across industries are turning to generative AI foundation models (FMs) to enhance their applications. To achieve optimal performance for specific use cases, customers are adopting and adapting these FMs to their unique domain requirements. This need for customization has become even more pronounced with the emergence of newContinue Reading

In: Artificial Intelligence

Recent advances in generative AI have led to the proliferation of new generation of conversational AI assistants powered by foundation models (FMs). These latency-sensitive applications enable real-time text and voice interactions, responding naturally to human conversations. Their applications span a variety of sectors, including customer service, healthcare, education, personal andContinue Reading