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Evaluating Your Generative AI Solution Responses using Microsoft.Extensions.AI.Evaluation
- Evaluating generative AI solutions is crucial for ensuring coherence, accuracy, and relevance of responses.
- Microsoft.Extensions.AI.Evaluation package provides libraries for code-level evaluations against language model integrations.
- Metrics include relevance, truth, completeness, fluency, coherence, equivalence, and groundedness.
- By quantifying and identifying problematic outputs, AI solutions can be continuously improved for trust and effectiveness.
- The evaluation process involves instantiating evaluators, executing evaluations, and validating results.
- An example of setting up a Truth Evaluation using the package is detailed with test code included.
- The importance of evaluating generative AI responses lies in ensuring quality, reliability, and trust in AI applications.
- The package helps in systematically assessing important metrics to rectify issues and enhance performance.
- Continuous evaluation and improvement lead to building trustworthy and responsible AI solutions.
- Further resources and links are provided for a deeper understanding of evaluation metrics for generative AI.
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