Fallback and error handling in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Why use fallback mechanisms in AI systems?

Imagine you have an AI chatbot that sometimes fails to understand user questions. Why is it important to have a fallback mechanism?

A To provide a default response or alternative action when the AI cannot answer correctly

B To make the AI ignore user inputs it does not understand

C To stop the AI from responding to any question after one failure

D To increase the AI's training data automatically without human help

💡 Hint

Think about what happens when the AI does not know an answer. What should it do instead of failing silently?

Fallback mechanisms help AI systems handle situations where they cannot provide a confident answer. This improves user experience by giving a default or alternative response instead of failing.

❓ Predict Output

intermediate

Output of fallback function in AI response

What is the output of the following Python code simulating a fallback in AI response?

Prompt Engineering / GenAI

def ai_response(user_input):
    responses = {'hello': 'Hi there!', 'bye': 'Goodbye!'}
    return responses.get(user_input, 'Sorry, I did not understand that.')

print(ai_response('hello'))
print(ai_response('thanks'))

A Hi there!\nNone

B Sorry, I did not understand that.\nSorry, I did not understand that.

C Hi there!\nSorry, I did not understand that.

D None\nNone

💡 Hint

Look at how the get method works with a default value.

The get method returns the value for the key if found; otherwise, it returns the default string. So for 'hello', it returns 'Hi there!'. For 'thanks', it returns the fallback message.

❓ Model Choice

advanced

Choosing a model with built-in error handling

You want to deploy an AI model that can gracefully handle uncertain inputs by providing fallback answers. Which model type is best suited for this?

A A model trained only on a small fixed dataset without validation

B A deterministic rule-based model without uncertainty estimation

C A model that always outputs the most frequent answer regardless of input

D A probabilistic model that outputs confidence scores with fallback thresholds

💡 Hint

Think about how the model can know when it is unsure.

Probabilistic models that provide confidence scores allow the system to detect low confidence and trigger fallback responses, improving robustness.

❓ Hyperparameter

advanced

Setting fallback threshold in AI confidence scores

In an AI system using confidence scores to decide when to fallback, which hyperparameter setting best balances accuracy and fallback frequency?

A Set a moderate confidence threshold to fallback only when confidence is low but not too often

B Disable confidence threshold and never fallback

C Set a very high confidence threshold so fallback triggers often, annoying users

D Set a very low confidence threshold so fallback triggers rarely, risking wrong answers

💡 Hint

Think about avoiding too many wrong answers and too many fallback messages.

A moderate threshold balances between giving correct answers and using fallback when unsure, improving user experience.

🔧 Debug

expert

Identify error in fallback implementation code

What is the output of the following Python code?

Prompt Engineering / GenAI

def fallback_response(input_text):
    try:
        result = 10 / int(input_text)
    except ValueError:
        return 'Input is not a number.'
    except ZeroDivisionError:
        return 'Cannot divide by zero.'
    return f'Result is {result}'

print(fallback_response('0'))

A Outputs: 'Input is not a number.'

B Outputs: 'Cannot divide by zero.'

C Raises TypeError

D Outputs: 'Result is 0'

💡 Hint

What happens when dividing by zero in Python?

Dividing by zero raises a ZeroDivisionError, which is caught and returns the fallback message.