Answer Relevance

The evaluation metric, Answer Relevancy, focuses on assessing how pertinent the generated answer is to the given prompt. A lower score is assigned to answers that are incomplete or contain redundant information and higher scores indicate better relevancy. This metric is computed using the question, the context and the answer.

The Answer Relevancy is defined as the mean cosine similartiy of the original question to a number of artifical questions, which where generated (reverse engineered) based on the answer:

\[\text{answer relevancy} = \frac{1}{N} \sum_{i=1}^{N} cos(E_{g_i}, E_o)\]
\[\text{answer relevancy} = \frac{1}{N} \sum_{i=1}^{N} \frac{E_{g_i} \cdot E_o}{\|E_{g_i}\|\|E_o\|}\]


  • \(E_{g_i}\) is the embedding of the generated question \(i\).

  • \(E_o\) is the embedding of the original question.

  • \(N\) is the number of generated questions, which is 3 default.

Please note, that eventhough in practice the score will range between 0 and 1 most of the time, this is not mathematically guranteed, due to the nature of the cosine similarity ranging from -1 to 1.


This is reference free metric. If you’re looking to compare ground truth answer with generated answer refer to answer_correctness

An answer is deemed relevant when it directly and appropriately addresses the original question. Importantly, our assessment of answer relevance does not consider factuality but instead penalizes cases where the answer lacks completeness or contains redundant details. To calculate this score, the LLM is prompted to generate an appropriate question for the generated answer multiple times, and the mean cosine similarity between these generated questions and the original question is measured. The underlying idea is that if the generated answer accurately addresses the initial question, the LLM should be able to generate questions from the answer that align with the original question.


Question: Where is France and what is it’s capital?

Low relevance answer: France is in western Europe.

High relevance answer: France is in western Europe and Paris is its capital.


Answer relevancy
from datasets import Dataset 
from ragas.metrics import answer_relevancy
from ragas import evaluate

data_samples = {
    'question': ['When was the first super bowl?', 'Who won the most super bowls?'],
    'answer': ['The first superbowl was held on Jan 15, 1967', 'The most super bowls have been won by The New England Patriots'],
    'contexts' : [['The First AFL–NFL World Championship Game was an American football game played on January 15, 1967, at the Los Angeles Memorial Coliseum in Los Angeles,'], 
    ['The Green Bay Packers...Green Bay, Wisconsin.','The Packers compete...Football Conference']],
dataset = Dataset.from_dict(data_samples)
score = evaluate(dataset,metrics=[answer_relevancy])


To calculate the relevance of the answer to the given question, we follow two steps:

  • Step 1: Reverse-engineer ‘n’ variants of the question from the generated answer using a Large Language Model (LLM). For instance, for the first answer, the LLM might generate the following possible questions:

    • Question 1: “In which part of Europe is France located?”

    • Question 2: “What is the geographical location of France within Europe?”

    • Question 3: “Can you identify the region of Europe where France is situated?”

  • Step 2: Calculate the mean cosine similarity between the generated questions and the actual question.

The underlying concept is that if the answer correctly addresses the question, it is highly probable that the original question can be reconstructed solely from the answer.