Human evaluation frameworks in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a simple human evaluation metric function that returns the average score.

Prompt Engineering / GenAI

def average_score(scores):
    return sum(scores) / [1]

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

Alen(scores)

Bsum(scores)

Cmax(scores)

Dmin(scores)

Attempts:

3 left

💡 Hint

Common Mistakes

Dividing by sum(scores) instead of the count.

Using max or min instead of the count.

To find the average score, divide the sum of scores by the number of scores, which is given by len(scores).

2fill in blank

medium

Complete the code to calculate the inter-rater agreement using Cohen's kappa formula denominator.

Prompt Engineering / GenAI

def cohen_kappa_denominator(p0, pe):
    return [1] - pe

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

A1

Bpe

Cp0

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using p0 instead of 1 in the denominator.

Subtracting p0 from pe instead of 1 from pe.

The denominator in Cohen's kappa is 1 minus the expected agreement pe.

3fill in blank

hard

Fix the error in the code to compute the average human evaluation score from a dictionary of scores.

Prompt Engineering / GenAI

def average_human_score(scores_dict):
    total = sum(scores_dict.values())
    count = len([1])
    return total / count

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

Ascores_dict.keys()

Bscores_dict

Cscores_dict.items()

Dscores_dict.values()

Attempts:

3 left

💡 Hint

Common Mistakes

Using len(scores_dict.values()) which is valid but redundant here.

Using len(scores_dict.items()) which also works but is less direct.

len(scores_dict) gives the number of entries, which is the count of scores.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that filters human evaluation scores above 3.

Prompt Engineering / GenAI

filtered_scores = [1]: score for [2], score in scores.items() if score > 3}

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

Arater

Bscore

Crater_id

Dscore_value

Attempts:

3 left

💡 Hint

Common Mistakes

Using the score as the key instead of the rater ID.

Mixing variable names inconsistently.

The dictionary keys are rater_id and the loop variable for keys is rater.

5fill in blank

hard

Fill all three blanks to compute the weighted average human evaluation score.

Prompt Engineering / GenAI

def weighted_average(scores, weights):
    total_weighted = sum(scores[i] * [1] for i in range(len(scores)))
    total_weight = sum([2])
    return total_weighted / [3]

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

Aweights[i]

Bweights

Ctotal_weight

Dscores[i]

Attempts:

3 left

💡 Hint

Common Mistakes

Using scores[i] instead of weights[i] in multiplication.

Dividing by weights instead of total_weight.

Multiply each score by its weight, sum weights, then divide total weighted sum by total weight.

Practice

1. What is the main purpose of human evaluation frameworks in AI?

easy

A. To have people judge AI outputs for quality

B. To replace all automatic scoring methods

C. To train AI models faster

D. To collect data without human input

Solution

1. Step 1: Understand the role of human evaluation

   Human evaluation frameworks involve people assessing AI outputs to check quality.

2. Step 2: Compare with other options

   Options B, C, and D do not describe the main purpose correctly; human evaluation does not replace all automatic methods, nor is it for training or data collection without humans.

3. Final Answer:

   To have people judge AI outputs for quality -> Option A

4. Quick Check:

   Human evaluation = people judge AI outputs [OK]

Hint: Human evaluation means people check AI output quality [OK]

Common Mistakes:

• Thinking human evaluation replaces automatic scores
• Confusing evaluation with training
• Assuming no human input is involved

2. Which of the following is a common method used in human evaluation frameworks?

easy

A. Simple rating scales

B. Automatic precision scoring

C. Gradient descent optimization

D. Data augmentation

Solution

1. Step 1: Identify common human evaluation methods

   Simple rating scales are widely used for humans to rate AI outputs.

2. Step 2: Eliminate unrelated options

   Automatic precision scoring, gradient descent, and data augmentation are technical methods not involving human judgment.

3. Final Answer:

   Simple rating scales -> Option A

4. Quick Check:

   Human evaluation uses rating scales [OK]

Hint: Look for methods involving human ratings or comparisons [OK]

Common Mistakes:

• Choosing automatic or technical AI training methods
• Confusing human evaluation with model training
• Ignoring the human aspect in options

3. Consider a human evaluation where 3 raters score AI responses on a scale from 1 to 5. The scores for one response are [4, 5, 3]. What is the average score?

medium

A. 3

B. 5

C. 4

D. 12

Solution

1. Step 1: Sum the scores given by raters

   4 + 5 + 3 = 12

2. Step 2: Calculate the average score

   Average = Total sum / Number of raters = 12 / 3 = 4

3. Final Answer:

   4 -> Option C

4. Quick Check:

   (4+5+3)/3 = 4 [OK]

Hint: Add scores then divide by number of raters [OK]

Common Mistakes:

• Adding but forgetting to divide by number of raters
• Choosing the sum instead of average
• Mixing up the scale values

4. A human evaluation study uses a comparison method where raters choose the better of two AI outputs. The code below has an error. What is the error?

def compare_outputs(output1, output2, rater_choice):
    if rater_choice == 'output1':
        return output1
    elif rater_choice == 'output2':
        return output2

result = compare_outputs('Answer A', 'Answer B', 'output3')
print(result)

medium

A. The comparison method cannot use strings as choices

B. The function should return both outputs instead of one

C. The print statement is missing parentheses

D. The function does not handle invalid rater choices properly

Solution

1. Step 1: Trace the code execution for invalid input

   For rater_choice='output3', neither condition matches, so no explicit return; function implicitly returns None.

2. Step 2: Identify the error

   Returning None for invalid choices is improper handling. Should explicitly manage invalid inputs (e.g., return error message or raise exception).

3. Final Answer:

   The function does not handle invalid rater choices properly -> Option D

4. Quick Check:

   Invalid input -> returns None [OK]

Hint: Check how function handles unexpected inputs [OK]

Common Mistakes:

• Assuming print syntax error in Python 3
• Thinking function must return both outputs
• Ignoring the lack of else clause handling

5. You want to design a human evaluation framework to compare two AI chatbots. Which approach best balances simplicity and detailed feedback?

hard

A. Use only open-ended feedback without ratings

B. Use simple rating scales plus side-by-side output comparisons

C. Use automatic BLEU scores without human input

D. Use complex statistical models without human ratings

Solution

1. Step 1: Consider evaluation goals

   Balancing simplicity and detail means combining easy-to-use ratings with meaningful comparisons.

2. Step 2: Evaluate options

   Use only open-ended feedback without ratings lacks ratings, making quantitative comparison hard. Use automatic BLEU scores without human input and D exclude human input, missing human judgment. Use simple rating scales plus side-by-side output comparisons combines ratings and comparisons, fitting the goal.

3. Final Answer:

   Use simple rating scales plus side-by-side output comparisons -> Option B

4. Quick Check:

   Simple ratings + comparisons = balanced evaluation [OK]

Hint: Combine ratings with comparisons for best feedback [OK]

Common Mistakes:

• Ignoring human input in evaluation
• Choosing only open feedback without structure
• Relying solely on automatic metrics