On custom evaluation frameworks for clinical RAG systems, showing why domain-specific metrics matter more than plug-and-play solutions when trust and safety are non-negotiable.
In this Vellum AI pricing guide, we discuss the costs, features, and value Vellum AI provides to help you decide if it’s the right investment for your business.
In this Semantic Kernel vs Autogen article, we explain the differences between the two frameworks and conclude which one is best suited for building AI agents.
Are your query rewriting strategies silently hurting your Retrieval-Augmented Generation (RAG) system? Small but unnoticed query errors can quickly degrade user experience, accuracy, and trust. Learn how ZenML's automated evaluation pipelines can systematically detect, measure, and resolve these hidden issues—ensuring that your RAG implementations consistently provide relevant, trustworthy responses.
Practical lessons on prompt engineering in production settings, drawn from real LLMOps case studies. It covers key aspects like designing structured prompts (demonstrated by Canva's incident review system), implementing iterative refinement processes (shown by Fiddler's documentation chatbot), optimizing prompts for scale and efficiency (exemplified by Assembled's test generation system), and building robust management infrastructure (as seen in Weights & Biases' versioning setup). Throughout these examples, the focus remains on systematic improvement through testing, human feedback, and error analysis, while balancing performance with operational costs and complexity.
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