Fallback and error handling in Prompt Engineering / GenAI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is fallback in the context of AI systems?

Fallback is a backup plan where the system provides a safe or default response when it cannot understand or process the input properly.

beginner

Why is error handling important in AI applications?

Error handling helps AI systems avoid crashes and gives users clear messages or alternative options when something goes wrong.

intermediate

Name one common method to handle errors in AI model predictions.

One common method is to check the confidence score of the prediction and if it is too low, trigger a fallback response or ask the user for clarification.

intermediate

How can fallback improve user trust in AI systems?

Fallback ensures users get meaningful responses even when the AI is unsure, making the system seem more reliable and less frustrating.

advanced

What is a graceful degradation in AI error handling?

Graceful degradation means the AI system continues to work in a simpler or limited way instead of failing completely when errors happen.

What should an AI system do when it cannot confidently answer a user's question?

A Provide a fallback response or ask for clarification

B Ignore the question and stay silent

C Give a random answer

D Crash the system

Fallback responses or asking for clarification help maintain good user experience and avoid confusion.

Which of the following is NOT a good practice in AI error handling?

A Logging errors for later review

B Providing clear error messages to users

C Hiding all errors without informing users

D Using fallback responses when uncertain

Hiding errors without informing users can cause confusion and reduce trust.

What does a low confidence score in AI prediction usually indicate?

A The AI is very sure about its answer

B The AI is unsure and might be wrong

C The AI has no data to answer

D The AI is ignoring the input

Low confidence means the AI is unsure and fallback or clarification is needed.

What is graceful degradation in AI systems?

A System continues working in a simpler way when errors occur

B System restarts automatically on error

C System hides errors from users

D System stops working immediately on error

Graceful degradation means the system keeps working with limited features instead of failing completely.

Fallback responses help AI systems to:

A Ignore user inputs

B Avoid any user interaction

C Always give the exact answer

D Crash less and keep users engaged

Fallback responses prevent crashes and keep the conversation going even when AI is unsure.

Explain what fallback and error handling mean in AI and why they are important.

Think about how AI behaves when it doesn't understand or makes mistakes.

Describe how confidence scores can be used to trigger fallback mechanisms in AI systems.

Consider how AI decides if it is sure enough to answer.

Practice

1. What is the main purpose of fallback mechanisms in AI systems?

easy

A. To provide alternative responses when the main AI model fails

B. To speed up the training process of the AI model

C. To increase the size of the AI model

D. To reduce the amount of data needed for training

Solution

1. Step 1: Understand fallback role

   Fallback mechanisms help AI systems handle failures gracefully by providing alternatives.

2. Step 2: Compare options

   Only To provide alternative responses when the main AI model fails describes providing alternative responses when the main AI fails, matching fallback purpose.

3. Final Answer:

   To provide alternative responses when the main AI model fails -> Option A

4. Quick Check:

   Fallback = alternative response [OK]

Hint: Fallback means backup plan for AI errors [OK]

Common Mistakes:

• Confusing fallback with training speed
• Thinking fallback reduces data size
• Assuming fallback increases model size

2. Which Python syntax correctly catches errors during AI model prediction?

easy

A. if error:
prediction = fallback_response

B. catch Exception:
prediction = fallback_response

C. try:
prediction = model.predict(data)
except Exception:
prediction = fallback_response

D. try:
prediction = model.predict(data)
finally:
prediction = fallback_response

Solution

1. Step 1: Identify correct error handling syntax

   Python uses try-except blocks to catch errors, as shown in try: prediction = model.predict(data) except Exception: prediction = fallback_response.

2. Step 2: Check other options

   if error: prediction = fallback_response uses invalid syntax, C uses wrong keyword 'catch', D uses finally which always runs, not only on error.

3. Final Answer:

   try-except block catching Exception -> Option C

4. Quick Check:

   Python error handling = try-except [OK]

Hint: Use try-except to catch errors in Python [OK]

Common Mistakes:

• Using 'catch' instead of 'except'
• Misusing 'finally' for error catching
• Using if statements to catch exceptions

3. What will be the output of this code snippet?

def get_response(input_text):
    try:
        return model.generate(input_text)
    except Exception:
        return "Sorry, I can't process that right now."

print(get_response('Hello'))

Assuming model.generate raises an exception, what prints?

medium

A. None

B. "Hello"

C. Exception error message

D. "Sorry, I can't process that right now."

Solution

1. Step 1: Analyze try-except behavior

   The function tries to run model.generate. If it raises an exception, the except block returns the fallback string.

2. Step 2: Determine output when exception occurs

   Since exception occurs, the except block returns "Sorry, I can't process that right now." which is printed.

3. Final Answer:

   "Sorry, I can't process that right now." -> Option D

4. Quick Check:

   Exception triggers fallback message [OK]

Hint: Exception triggers except block return [OK]

Common Mistakes:

• Assuming original input prints
• Expecting unhandled exception error
• Thinking function returns None

4. Identify the error in this fallback code snippet:

try:
    result = model.predict(data)
except:
    result = fallback()
print(result)

What is the main issue?

medium

A. The fallback function is not defined or imported

B. The try block is missing a return statement

C. The except block should specify the exception type

D. The print statement is inside the except block

Solution

1. Step 1: Check except block usage

   Using except without specifying exception type is allowed but not best practice; not an error.

2. Step 2: Verify fallback function usage

   If fallback() is not defined or imported, calling it causes a NameError, which is the main issue.

3. Final Answer:

   Fallback function is not defined or imported -> Option A

4. Quick Check:

   Undefined fallback() causes error [OK]

Hint: Undefined fallback() causes runtime error [OK]

Common Mistakes:

• Thinking except must specify exception
• Assuming print must be inside except
• Believing try needs return statement

5. You want to build an AI chatbot that always replies, even if the main model fails. Which approach best ensures this fallback behavior?

hard

A. Only use the fallback response without the main model

B. Use try-except to catch errors and return a simple default message

C. Ignore errors and let the system crash to fix bugs faster

D. Train the model longer to avoid any errors

Solution

1. Step 1: Understand fallback goal

   The goal is to always reply, even if the main model fails, so fallback must catch errors.

2. Step 2: Evaluate options for fallback

   Use try-except to catch errors and return a simple default message uses try-except to catch errors and return a default message, ensuring reply always.

3. Step 3: Reject other options

   Training longer (B) doesn't guarantee no errors; ignoring errors (C) causes crashes; only fallback (A) loses AI benefits.

4. Final Answer:

   Use try-except to catch errors and return a simple default message -> Option B

5. Quick Check:

   Try-except + default reply = reliable fallback [OK]

Hint: Try-except with default reply ensures fallback [OK]

Common Mistakes:

• Thinking longer training removes all errors
• Ignoring errors to fix bugs faster
• Using only fallback loses AI responses