Remove Guidelines Remove Metrics Remove User Experience
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Ground truth curation and metric interpretation best practices for evaluating generative AI question answering using FMEval

AWS Machine Learning

This post focuses on evaluating and interpreting metrics using FMEval for question answering in a generative AI application. FMEval is a comprehensive evaluation suite from Amazon SageMaker Clarify , providing standardized implementations of metrics to assess quality and responsibility. Question Answer Fact Who is Andrew R.

Metrics 95
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10 Strategies for Optimizing SaaS Design: Leveraging the Psychology of Colors for Positive User Experiences

SurveySensum

Aligning color with emotion lets you create a positive and engaging user experience, and optimize SaaS design. Ensuring Accessibility Everyone deserves a smooth SaaS experience, including the estimated 300 million people globally who experience color blindness or visual impairments. Feeling lost? Want them to take action?

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Metrics for evaluating content moderation in Amazon Rekognition and other content moderation services

AWS Machine Learning

Content moderation is the process of screening and monitoring user-generated content online. To provide a safe environment for both users and brands, platforms must moderate content to ensure that it falls within preestablished guidelines of acceptable behavior that are specific to the platform and its audience. Total FP > 0.

Metrics 91
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Drive efficiencies with CI/CD best practices on Amazon Lex

AWS Machine Learning

Identify data handling requirements and configure appropriate controls – Amazon Lex follows the AWS shared responsibility model , which includes guidelines for data protection to comply with industry regulations and with your company’s own data privacy standards. Such test data can provide experience validation for your target customer base.

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Build well-architected IDP solutions with a custom lens – Part 4: Performance efficiency

AWS Machine Learning

To build an enterprise solution, developer resources, cost, time and user-experience have to be balanced to achieve the desired business outcome. To follow along with this post, you should be familiar with the previous posts in this series ( Part 1 and Part 2 ) and the guidelines in Guidance for Intelligent Document Processing on AWS.

Metrics 120
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Best practices for building robust generative AI applications with Amazon Bedrock Agents – Part 1

AWS Machine Learning

Crafting the user experience: Planning agent tone and greetings The personality of your agent sets the tone for the entire user interaction. Carefully planning the tone and greetings of your agent is crucial for creating a consistent and engaging user experience.

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Evaluating prompts at scale with Prompt Management and Prompt Flows for Amazon Bedrock

AWS Machine Learning

Many organizations struggle to consistently create and effectively evaluate their prompts across their various applications, leading to inconsistent performance and user experiences and undesired responses from the models. Specificity matters – Be as specific as possible in your prompts and evaluation criteria.