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Awesome GitHub RepositoriesLLM Workflow Optimization
Analyzing model-specific metrics and usage data to refine the logic of generative AI pipelines.
Distinct from Application Performance Optimization: Specializes application performance optimization for the specific logic and bottlenecks of LLM workflows.
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Awesome LLM Workflow Optimization GitHub Repositories
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- explodinggradients/ragas
explodinggradients/ragas
14,400Auf GitHub ansehenRagas is an evaluation framework and performance benchmark designed to quantify the quality of retrieval augmented generation pipelines. It functions as an application optimizer to identify bottlenecks in language model workflows using automated metrics and model-based scoring. The framework includes a system for generating synthetic datasets that mimic production scenarios and edge cases to create realistic test cases. It enables reference-free assessment, allowing the evaluation of response quality by analyzing grounding in the provided context without requiring gold-standard labels. The s
Analyzes live application data and output scores to identify bottlenecks in language model workflows.
Python
