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Feedback and Analysis

Feedback and analysis play a crucial role in the continuous improvement of Ayushma's AI assistant. By gathering feedback from admins and reviewers and analyzing test run results, Ayushma can identify areas for enhancement, refine its responses, and ensure the delivery of accurate and reliable medical information.

Providing Feedback on Test Cases​

  • Qualitative Feedback: Admins and reviewers can provide qualitative feedback on individual test cases within a test run. This feedback can include observations about the AI's responses, such as:
    • Accuracy and Relevance: Assessing whether the AI's answer accurately addresses the question and is relevant to the medical context.
    • Completeness: Evaluating whether the AI's response provides a comprehensive and informative answer or if it lacks important details.
    • Clarity and Coherence: Judging the clarity and coherence of the AI's language and whether the response is easy to understand.
    • Safety and Bias: Identifying any potential safety concerns or biases present in the AI's response.
  • Rating Systems: Ayushma might implement rating systems to allow for quick and standardized feedback on test cases. These ratings could be based on scales such as:
    • Accuracy: Rating the factual correctness of the AI's response.
    • Helpfulness: Assessing the overall usefulness and value of the AI's answer.
    • Safety: Evaluating the safety and potential risks associated with the AI's response in a medical context.
  • Feedback Interface: Ayushma should provide a user-friendly interface for submitting feedback, allowing admins and reviewers to easily input their observations and ratings.

Analyzing Test Run Data​

  • Quantitative Metrics: Ayushma typically provides quantitative metrics to assess the AI assistant's performance across test runs, such as:
    • Average Cosine Similarity: This metric measures the semantic similarity between the AI's responses and the expected answers, indicating how well the AI understands the meaning and context of the questions.
    • Average BLEU Score: BLEU score evaluates the overlap and fluency of the AI's responses compared to the reference answers, providing insights into the quality and grammatical correctness of the generated text.
    • Pass/Fail Rate: This metric shows the percentage of test cases where the AI's response met predefined criteria for success, offering a high-level overview of the AI's overall performance.
  • Visualization: Ayushma may utilize charts, graphs, and other visual representations to present test run data in a clear and informative manner. Visualizations can help identify trends, patterns, and outliers in the AI's performance.
  • Comparative Analysis: Admins can compare the results of multiple test runs to track the AI assistant's progress over time, assess the impact of model updates or parameter adjustments, and identify areas where further improvement is needed.

Benefits of Feedback and Analysis​

  • Targeted Improvements: Feedback and analysis help pinpoint specific areas where the AI assistant's performance can be enhanced, allowing for targeted interventions and optimizations.
  • Model Refinement: Insights gained from feedback and analysis can inform the refinement of AI models, training data, and system parameters, leading to more accurate and reliable responses.
  • Bias Detection and Mitigation: Analyzing feedback and test run data can reveal potential biases in the AI's responses, enabling admins to take corrective actions and ensure fairness and inclusivity.
  • User Satisfaction: By continuously improving the AI assistant's performance based on feedback and analysis, Ayushma can enhance user satisfaction and trust in the platform's capabilities.