Databricks Generative AI Engineer Associate Question 84
Select 3You are building a retrieval-augmented generation (RAG) system using Databricks and need to evaluate the retrieval performance of your system. Which of the following tools or metrics would be most appropriate to assess the quality of the retrieved documents in terms of their relevance to user queries?
- A
Precision at K (P@K)
- B
Recall
- C
F1 Score
- D
Mean Reciprocal Rank (MRR)
- E
Word Error Rate (WER)
Show answer and explanation
Correct answers: A, B, D
Explanation
Evaluating retrieval performance requires metrics that assess the relevance of retrieved documents. Precision at K (P@K), Recall, and Mean Reciprocal Rank (MRR) are standard metrics that measure the quality of retrieval systems. P@K focuses on precision within the top results, Recall evaluates the completeness of retrieval, and MRR captures the ranking effectiveness. Metrics like F1 Score and WER are not applicable for retrieval tasks as they are designed for other types of evaluations such as classification or speech recognition.
- A. Correct.
Precision at K (P@K) measures the proportion of relevant documents among the top K retrieved documents. It is a widely used metric to evaluate retrieval quality.
- B. Correct.
Recall measures the proportion of relevant documents retrieved out of all relevant documents available in the dataset. It is essential to evaluate how well the system captures the complete set of relevant information.
- C. Incorrect.
F1 Score combines precision and recall, but it is more commonly used in classification problems rather than in retrieval evaluation specifically.
- D. Correct.
Mean Reciprocal Rank (MRR) evaluates the rank position of the first relevant document in the retrieved list. It is particularly useful for systems where users expect the most relevant results at the top.
- E. Incorrect.
Word Error Rate (WER) is a metric used in speech recognition systems to evaluate transcription errors, and it is not relevant for evaluating retrieval performance.