Databricks Generative AI Engineer Associate Question 83
Select 3You are building a retrieval-augmented generation (RAG) system using Databricks and want to evaluate the performance of your retrieval model. After running queries and retrieving documents, you decide to use metrics to assess the quality of retrieval. Which of the following metrics are appropriate for evaluating retrieval performance?
- A
Precision at K (P@K)
- B
Mean Reciprocal Rank (MRR)
- C
Word Error Rate (WER)
- D
Mean Average Precision (MAP)
- E
F-1 Score
Show answer and explanation
Correct answers: A, B, D
Explanation
To evaluate retrieval performance, appropriate metrics focus on the relevance and ranking of retrieved documents. Precision at K (P@K) assesses relevance in the top K results, Mean Reciprocal Rank (MRR) evaluates ranking performance, and Mean Average Precision (MAP) measures overall retrieval quality. Word Error Rate (WER) and F-1 Score are not suitable for retrieval tasks as they are designed for other types of problems.
- A. Correct.
Precision at K (P@K) measures the proportion of relevant documents among the top K retrieved documents, making it a standard metric for evaluating retrieval performance.
- B. Correct.
Mean Reciprocal Rank (MRR) evaluates how well the retrieval system ranks the most relevant document, which is crucial for retrieval systems.
- C. Incorrect.
Word Error Rate (WER) is used to evaluate transcription tasks (e.g., in speech-to-text systems) and is not relevant for retrieval performance evaluation.
- D. Correct.
Mean Average Precision (MAP) is a widely used metric that measures retrieval performance by considering precision at multiple recall levels, making it an appropriate choice.
- E. Incorrect.
F-1 Score is typically used in classification tasks and is not directly applicable to evaluating retrieval systems.