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  • Conversational language understanding best practices - Azure AI . . .
    Data in a conversational language understanding project can have two datasets: a testing set and a training set If you want to use multiple test sets to evaluate your model, you can: Give your test sets different names (for example, "test1" and "test2")
  • How do I Evaluate my LLM Chatbot? | Microsoft Community Hub
    LLM Based evaluation methods have become very popular for a wide variety of natural language task assessment [Li et al 2024] Common LLM assessed metrics include response Coherence, Fluency, Consistency, and Relevance to a given question or context
  • Evaluate the quality of your AI applications with ease
    Use your favorite Testing Framework (e g , MSTest, xUnit, or NUnit) and testing workflows (Test Explorer, dotnet test, CI CD pipeline) to evaluate your applications The library also provides easy ways to do online evaluations of your application through publishing evaluation scores to telemetry and monitoring dashboards
  • RUBICON: Rubric-Based Evaluation of Domain-Specific Human AI Conversations
    In this paper, we propose RUBICON, a technique for evaluating domain-specific Human-AI conversations RUBICON comprises three main components: (1) rubric set generation, (2) rubric selec-tion, and (3) conversation evaluation
  • #AI102 - Build a Conversational Language Understanding Model with Azure . . .
    Attendees will learn how to train, test, and deploy a conversational model that can understand user inputs and provide relevant responses This session covers essential topics such as defining intents, using patterns to differentiate similar utterances, and leveraging pre-built entity components
  • Microsoft Conversational AI tools enable developers to build, connect . . .
    Bot Builder Tools are designed to enable an end-to-end conversational app-centric development workflow From planning and testing mockups, through adding intelligence to your bot with LUIS models and QnAMaker knowledge bases, to publishing your bot and connecting with your audience using Azure Bot Services supported channels
  • How to train and evaluate models in Conversational Language . . .
    Standard training allows you to add utterances and test them quickly at no cost The evaluation scores shown should guide you on where to make changes in your project and add more utterances Once you’ve iterated a few times and made incremental improvements, you can consider using advanced training to train another version of your model
  • The Ultimate Guide to Testing Conversational AI: Challenges Best . . .
    AI-infused testing uses LLMs to evaluate conversational coherence, assess tone and empathy, or simulate diverse user personas to stress-test bots at scale It also includes self-healing scripts that adapt to changes in the UI or API layers and feedback loops that retrain NLU models based on failure patterns
  • 8 Best Practices for Automated Testing in Conversational AI - Cognigy
    To help you better understand and leverage this potent feature, in our latest Help Center article, Cognigy experts outline eight best practices to QA your virtual agents with Playbooks 1 Design comprehensive Playbooks 2 Leverage various Playbook creation methods 3 Choose the most effective Playbook Run method 4
  • Evaluating Language Models with Azure AI Studio: A Comprehensive Guide . . .
    With Azure AI Studio, you can evaluate language models in an efficient and scalable way, using a range of evaluation metrics and datasets In this blog post, we'll take a step-by-step look at how to evaluate language models using Azure AI Studio





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