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[EbramTawfik]

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✅ Integration Tests PASSED

Workflow: PR Validation
Commit: 40a637e4031b1ddaac9d9d04575ab11b13bc5e7f
Result: PASSED

🎉 All integration tests passed! This PR is ready for review.

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[shyamnamboodiripad]

It may be better to use some form of structured output. Also, it may be a good idea to ask the LLM to include chain-of-thought and reasoning for the score as this can generally help to improve the quality of the evaluation / scores produced by the LLM.

For example, the GroundednessEvaluator that ships as part of the Microsoft.Extensions.AI.Evaluation libraries employs some of the above techniques.

[shyamnamboodiripad]

It would be better to implement this as an IEvaluator. This function would essentially become IEvaluator.EvaluateAsync() for your evaluator. This function could then return an EvaluationResult that includes one or more metrics (in your case it could be a single NumericMetric with Name like "Match Score").

[shyamnamboodiripad]

I would recommend reading through and running the samples under https://github.com/dotnet/ai-samples/blob/main/src/microsoft-extensions-ai-evaluation/api/README.md. These samples are structured as a series of unit tests where each test builds upon concepts introduced in previous tests.

I would recommend going through the READMEs, instructions and code for these sample tests one test at a time to understand how the various APIs, concepts and functionality in the Microsoft.Extensions.AI.Evaluation libraries work, and how this functionality can be used within your tests to set up your own offline eval pipelines and reporting.

For example,

• expectedResponsePattern should ideally be passed using a derived EvaluationContext type that is tied to your new IEvaluator above (similar to how GroundednessEvaluatorContext is used to pass the GroundingContext to the GroundednessEvaluator).
• minimumAcceptableScore should ideally be configured by including a default 'interpretation' as part of the NumericMetric returned from your IEvaluator (similar to how GroundednessEvaluator returns a NumericMetric with an interpretation that considers the metric as poor iif the score is less than 2 and exceptional if it is greater than 4).
• See the samples under the Reporting folder such as Example01_SamplingAndEvaluatingSingleResponse() to understand the general anatomy of a single test including how the reporting configuration is set up to include the set of evaluators and the LLM connection (IChatClient), how tests create individual scenarios (each with its own unique scenarioName) , how the scores for these scenarios get persisted, and how reports can be generated using this persisted data.