Databricks Generative AI Engineer Associate Question 80
Select 3You are building a retrieval-augmented generation (RAG) system to provide answers to user queries based on a large document repository. To evaluate the performance of the retrieval system, you decide to use specific metrics. Which of the following metrics are appropriate for evaluating retrieval performance?
- A
Precision at K
- B
Mean Average Precision (MAP)
- C
Root Mean Squared Error (RMSE)
- D
Recall
- E
BLEU Score
Show answer and explanation
Correct answers: A, B, D
Explanation
Evaluating retrieval performance requires metrics that assess how effectively relevant documents are retrieved from a repository. Precision at K, Mean Average Precision (MAP), and Recall are standard metrics used for this purpose as they directly measure relevance and coverage. Metrics like RMSE and BLEU Score are unrelated to retrieval tasks and are used in other contexts such as regression or text generation.
- A. Correct.
Precision at K is a relevant metric for retrieval performance as it measures the proportion of relevant documents retrieved in the top K results, helping evaluate how accurate the retrieval system is.
- B. Correct.
Mean Average Precision (MAP) is an appropriate metric for evaluating retrieval performance, as it averages precision scores at various recall levels, providing a comprehensive view of system performance.
- C. Incorrect.
Root Mean Squared Error (RMSE) is not a suitable metric for retrieval performance as it is typically used to evaluate regression models, not retrieval systems.
- D. Correct.
Recall is a relevant retrieval metric as it measures the proportion of all relevant documents that were successfully retrieved, providing insights into the system's completeness.
- E. Incorrect.
BLEU Score is not applicable here as it is designed to evaluate the quality of machine-generated text (e.g., translations or summaries), not retrieval systems.