RAG evaluation metrics in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the recall score for RAG model predictions.

Prompt Engineering / GenAI

from sklearn.metrics import [1]
recall = [1](true_labels, predicted_labels)

Drag options to blanks, or click blank then click option'

Af1_score

Bprecision_score

Caccuracy_score

Drecall_score

Attempts:

3 left

💡 Hint

Common Mistakes

Using precision_score instead of recall_score

Using accuracy_score which is less informative for retrieval tasks

The recall_score function measures how many relevant items are selected by the model. It is important for RAG evaluation to check if the model retrieves all relevant information.

2fill in blank

medium

Complete the code to compute the F1 score for RAG model outputs.

Prompt Engineering / GenAI

from sklearn.metrics import [1]
f1 = [1](true_labels, predicted_labels)

Drag options to blanks, or click blank then click option'

Af1_score

Baccuracy_score

Crecall_score

Dprecision_score

Attempts:

3 left

💡 Hint

Common Mistakes

Using accuracy_score which does not balance precision and recall

Confusing precision_score with f1_score

The F1 score balances precision and recall, giving a single metric to evaluate RAG model performance.

3fill in blank

hard

Fix the error in the code to calculate precision for RAG evaluation.

Prompt Engineering / GenAI

from sklearn.metrics import precision_score
precision = precision_score(true_labels, [1])

Drag options to blanks, or click blank then click option'

Apredicted_labels

Btrue_labels

Cpredictions

Dlabels

Attempts:

3 left

💡 Hint

Common Mistakes

Passing true_labels twice

Using an undefined variable like 'labels'

The precision_score function requires the predicted labels as the second argument to compare against true labels.

4fill in blank

hard

Fill both blanks to create a dictionary of evaluation metrics for RAG.

Prompt Engineering / GenAI

metrics = {
    'precision': [1](true_labels, predicted_labels),
    'recall': [2](true_labels, predicted_labels)
}

Drag options to blanks, or click blank then click option'

Aprecision_score

Brecall_score

Cf1_score

Daccuracy_score

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up precision_score and recall_score

Using accuracy_score which is less relevant here

This dictionary stores precision and recall scores computed from true and predicted labels, key metrics for RAG evaluation.

5fill in blank

hard

Fill all three blanks to compute precision, recall, and F1 score for RAG evaluation.

Prompt Engineering / GenAI

results = {
    'precision': [1](true_labels, predicted_labels),
    'recall': [2](true_labels, predicted_labels),
    'f1': [3](true_labels, predicted_labels)
}

Drag options to blanks, or click blank then click option'

Aprecision_score

Brecall_score

Cf1_score

Daccuracy_score

Attempts:

3 left

💡 Hint

Common Mistakes

Using accuracy_score instead of f1_score

Swapping recall_score and precision_score

This code calculates the three key metrics to evaluate RAG models: precision, recall, and F1 score.

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