RAG evaluation metrics in Prompt Engineering / GenAI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does RAG stand for in machine learning?

RAG stands for Retrieval-Augmented Generation, a method combining retrieval of documents with text generation.

beginner

Why do we need evaluation metrics for RAG models?

Evaluation metrics help us measure how well the RAG model retrieves relevant information and generates accurate, useful answers.

intermediate

Name two common metrics used to evaluate the retrieval part of RAG.

Recall@k and Precision@k are common metrics to check if the model retrieves relevant documents within the top k results.

intermediate

What metric measures the quality of generated text in RAG?

BLEU, ROUGE, and METEOR are popular metrics to compare generated text with reference answers to measure quality.

beginner

How does Exact Match (EM) metric work in RAG evaluation?

Exact Match checks if the generated answer exactly matches the correct answer, giving a simple yes/no score.

Which metric checks if the correct document is among the top retrieved results in RAG?

A Exact Match

B BLEU

C Recall@k

D METEOR

Recall@k measures if the relevant document is found within the top k retrieved documents.

What does BLEU score evaluate in RAG models?

A Generated text quality

B Retrieval accuracy

C Training speed

D Model size

BLEU measures how closely the generated text matches reference answers.

Which metric gives a simple yes/no score if the generated answer matches exactly the correct answer?

A ROUGE

B Exact Match

C Recall@k

D Precision@k

Exact Match checks for exact equality between generated and correct answers.

Precision@k in RAG evaluation measures:

A Training loss

B How many relevant documents are missed

C Quality of generated text

D How many retrieved documents are relevant within top k

Precision@k measures the proportion of relevant documents among the top k retrieved.

Which metric is NOT typically used to evaluate the generation part of RAG?

A Recall@k

B ROUGE

C METEOR

D BLEU

Recall@k is used for retrieval evaluation, not generation.

Explain the difference between retrieval and generation evaluation metrics in RAG.

Think about the two main parts of RAG: finding info and writing answers.

Describe how Exact Match metric works and when it is useful in RAG evaluation.

Consider simple yes/no correctness.

Practice

1. What does RAG evaluation metrics primarily measure in a retrieval-augmented generation system?

easy

A. Both the quality of generated answers and the relevance of retrieved documents

B. Only the speed of document retrieval

C. The size of the training dataset

D. The number of layers in the neural network

Solution

1. Step 1: Understand RAG system components

   RAG combines document retrieval and answer generation, so evaluation must cover both parts.

2. Step 2: Identify what metrics measure

   Metrics check answer quality (like accuracy) and retrieval quality (like precision).

3. Final Answer:

   Both the quality of generated answers and the relevance of retrieved documents -> Option A

4. Quick Check:

   RAG metrics = answer + retrieval quality [OK]

Hint: RAG means check both answer and retrieval quality [OK]

Common Mistakes:

• Thinking RAG only measures answer quality
• Confusing retrieval speed with quality
• Ignoring document relevance in evaluation

2. Which of the following is a common metric used to evaluate the retrieval part of a RAG system?

easy

A. Mean squared error

B. BLEU score

C. Cross-entropy loss

D. Retrieval precision

Solution

1. Step 1: Identify retrieval metrics

   Retrieval precision measures how many retrieved documents are relevant.

2. Step 2: Match metric to retrieval

   BLEU is for text generation, cross-entropy and MSE are loss functions, not retrieval metrics.

3. Final Answer:

   Retrieval precision -> Option D

4. Quick Check:

   Retrieval metric = precision [OK]

Hint: Precision measures retrieval relevance, not BLEU or loss [OK]

Common Mistakes:

• Choosing BLEU which is for generation
• Confusing loss functions with evaluation metrics
• Ignoring retrieval-specific metrics

3. Consider this Python snippet evaluating a RAG model's answer quality using F1 score:

from sklearn.metrics import f1_score
true_answers = ["cat", "dog", "bird"]
pred_answers = ["cat", "dog", "cat"]
f1 = f1_score(true_answers, pred_answers, average='macro')
print(round(f1, 2))

What will be the output?

medium

A. Error due to string inputs

B. 0.75

C. 0.56

D. 1.00

Solution

1. Step 1: Verify f1_score handles strings

   sklearn's f1_score supports string labels directly via internal encoding.

2. Step 2: Compute macro F1

   Classes: 'bird', 'cat', 'dog'
   • 'bird': F1 = 0 (TP=0, predicted 0 times)
   • 'cat': prec=1/2=0.5, rec=1/1=1, F1=2×0.5×1/(0.5+1)=0.67
   • 'dog': F1=1
   Macro F1 = (0 + 0.67 + 1)/3 ≈ 0.5556, round(0.56, 2) = 0.56

3. Final Answer:

   0.56 -> Option C

4. Quick Check:

   macro F1 = (0 + 0.67 + 1)/3 = 0.56 [OK]

Hint: f1_score works on strings; macro F1=(0+0.67+1)/3=0.56 [OK]

Common Mistakes:

• Computing micro F1 or accuracy (0.67)
• Expecting error due to strings
• Wrong per-class calculation (0.75)

4. You have this code snippet to compute retrieval precision but it gives wrong results:

retrieved_docs = ["doc1", "doc2", "doc3"]
relevant_docs = ["doc2", "doc4"]
precision = len(set(retrieved_docs) & set(relevant_docs)) / len(relevant_docs)
print(round(precision, 2))

What is the bug and how to fix it?

medium

A. Divide by len(retrieved_docs) instead of len(relevant_docs)

B. Use union instead of intersection in numerator

C. Convert lists to tuples before set operations

D. No bug, code is correct

Solution

1. Step 1: Understand precision formula

   Precision = relevant retrieved / total retrieved, so denominator must be retrieved docs count.

2. Step 2: Identify denominator mistake

   Code divides by len(relevant_docs), which is recall formula denominator.

3. Step 3: Fix denominator

   Change denominator to len(retrieved_docs) to compute precision correctly.

4. Final Answer:

   Divide by len(retrieved_docs) instead of len(relevant_docs) -> Option A

5. Quick Check:

   Precision denominator = retrieved docs count [OK]

Hint: Precision divides by retrieved docs count, not relevant docs [OK]

Common Mistakes:

• Mixing precision with recall formula
• Using union instead of intersection
• Ignoring set conversion issues

5. You want to evaluate a RAG model combining answer F1 score and retrieval precision into a single metric. Which approach is best to fairly combine these metrics?

hard

A. Add F1 score and retrieval precision directly

B. Calculate the harmonic mean of F1 score and retrieval precision

C. Use only the higher of the two scores

D. Multiply F1 score by retrieval precision without normalization

Solution

1. Step 1: Understand metric combination needs

   Combining metrics requires balancing both scores fairly, avoiding dominance by one.

2. Step 2: Evaluate combination methods

   Harmonic mean balances low and high values well; addition or multiplication can skew results.

3. Step 3: Choose harmonic mean

   Harmonic mean is common for combining precision and recall, so it suits combining F1 and retrieval precision.

4. Final Answer:

   Calculate the harmonic mean of F1 score and retrieval precision -> Option B

5. Quick Check:

   Harmonic mean balances combined metrics [OK]

Hint: Use harmonic mean to balance combined metrics fairly [OK]

Common Mistakes:

• Adding metrics without normalization
• Ignoring metric scale differences
• Choosing max score only