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Agent skills guide a developer through the repeated steps of clinical ASR evaluation: defining a profile, building a term-centered benchmark, reviewing pronunciations, generating synthetic audio, measuring ASR behavior, and choosing the next iteration.  In this post, the flywheel is the full improvement loop: build the benchmark, evaluate ASR behavior, use the results to decide what to change, and reevaluate after the change. The pipeline is one pass through part of that loop, such as generating sentences, adding pronunciation markup, synthesizing audio, and writing the manifest. The pipeline

NVIDIA AI Blueprints are customizable reference workflows for building generative AI pipelines. Developers can use NVIDIA AI Blueprints to build multimodal RAG pipelines. The RAG Blueprint is built on NVIDIA NeMo Retriever models for continuously indexing multimodal documents for fast and accurate semantic search at enterprise scale. The VSS Blueprint ingests massive volumes of streaming or archival video for search, summarization, interactive Q&A, and event-trigger actions such as alerting.

Pipeline friction refers to any obstacle that slows or disrupts the journey of a model from training to production inference. Unlike bugs that produce clear error messages, friction often manifests as subtle inefficiencies: a model that consumes twice the expected GPU memory, for example, or an inference server that drops requests under load, or a deployment that works on one GPU architecture but fails on another. The most frequent sources of pipeline friction can be grouped into four categories: Model export issues: These arise when converting from training frameworks like PyTorch or TensorFl

While model and agent evaluation are inextricably linked, their technical benchmarks and metrics for success are fundamentally different.