Awesome Infra for AI › LLM Evaluation & Testing

uptrain-ai/uptrain

⭐ 2367 Python repository created 2022-11-07

UpTrain is an open-source, unified platform designed specifically for the evaluation and improvement of Generative AI applications. It offers a comprehensive suite of tools to assess the quality and reliability of large language models (LLMs) and AI agents. The platform includes over 20 preconfigured checks covering various aspects such as language understanding, code generation, sentiment analysis, hallucination detection, toxicity, bias, and jailbreak attempts. These checks provide graded evaluations, allowing developers to quickly identify weaknesses in their models. Beyond basic evaluation, UpTrain performs root cause analysis on identified failure cases, helping uncover the underlying reasons for poor performance. It also provides actionable insights on how to resolve these issues, facilitating an iterative improvement process for AI applications. The platform supports various LLM use cases, including chatbots, agents, RAG systems, and code generation applications. It integrates with popular LLM providers like OpenAI, Azure OpenAI, Anthropic, and open-source models, allowing for flexible testing and evaluation across different environments. Key features include a no-code UI for test case generation, an SDK for custom evaluations, production monitoring for data and model drift, and a prompt experiments hub for managing and optimizing prompts. UpTrain aims to help organizations deploy reliable and robust AI applications by providing continuous evaluation, monitoring, and improvement capabilities throughout the AI development lifecycle. It's a critical tool for MLOps teams focused on the operational aspects of serving and maintaining high-quality generative AI.

https://github.com/uptrain-ai/uptrain

autoevaluationevaluationexperimentationhallucination-detectionjailbreak-detectionllm-evalllm-promptingllm-testllmopsmachine-learningmonitoringopenai-evalsprompt-engineeringroot-cause-analysisllm monitoringai agentsRAG evaluationprompt experimentation

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