Repository: explodinggradients/ragas
Issue: [Questions] How to change the model based on the metrics?
GitHub labels: question

Hello,

I hope, that you are doing well, I have a question, regarding metrics correlation to model quality. I have implemented all the metrics, which are provided. And now thinking in the way, how can I improve the model based on the metrics. Do you have any idea in which direction I need to take a look? So is it possible to have correlation between metrics and model "quality"?

For example, if some metric is low, I can make a conclusion, that the data for fine tuning of the model is in bad quality etc. Kinda get recommendation.

Thank you!

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Yes, it is possible to establish a correlation between metrics and model quality using the `AnswerCorrectness` metric from the `ragas` framework. If a certain metric, such as the `AnswerCorrectness` score, is low, it can indicate that the data used for fine-tuning the model is of poor quality. This metric evaluates the correctness of answers compared to ground truth by combining factuality and semantic similarity. A low score in this metric suggests that the model's answers are not well-aligned with the ground truth, which could be due to poor quality data used for fine-tuning <sup>[[1]](https://github.com/explodinggradients/ragas/blob/main/src/ragas/metrics/_answer_correctness.py)</sup>.

Additionally, the `ragas` framework provides various tools and metrics to monitor and improve your RAG (Retrieval Augmented Generation) application in production. These tools can help identify issues such as hallucinations, poor context retrievals, and harmful or evasive responses. By leveraging these insights, you can make informed decisions to enhance the quality of your fine-tuning data and overall model performance <sup>[[2]](https://github.com/explodinggradients/ragas/blob/main/docs/getstarted/monitoring.md)</sup>.

For example, the `ContextRecall` metric estimates context recall by evaluating the alignment between the retrieved context and the provided answer. A low `ContextRecall` score can indicate that the context retrieval process is not effective, which might be due to poor quality data or retrieval mechanisms <sup>[[3]](https://github.com/explodinggradients/ragas/blob/main/src/ragas/metrics/_context_recall.py)</sup>.

By using these metrics and tools, you can gain valuable insights into your model's performance and identify specific areas for improvement, ensuring that your fine-tuning data is of high quality <sup>[[4]](https://github.com/explodinggradients/ragas/blob/main/docs/concepts/metrics/index.md)</sup>.


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@zhuraromdev 
![](https://docs.ragas.io/en/stable/_static/imgs/component-wise-metrics.png)

this image might help you. The idea is you measure each component and figure out how to improve the generator part of retriever part