Output guardrails in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to set a maximum length for the generated text.

Prompt Engineering / GenAI

output = model.generate(input_text, max_length=[1])

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

Atemperature

Bmax_tokens

C50

Dstop_sequence

Attempts:

3 left

💡 Hint

Common Mistakes

Using parameters that control randomness instead of length.

The max_length parameter limits how many tokens the model generates, controlling output length.

2fill in blank

medium

Complete the code to stop generation when a specific word appears.

Prompt Engineering / GenAI

output = model.generate(input_text, stop=[1])

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

A'end'

B100

CTrue

Dtemperature

Attempts:

3 left

💡 Hint

Common Mistakes

Using numbers or booleans instead of strings for stop.

The stop parameter tells the model to stop generating when it sees the given string.

3fill in blank

hard

Fix the error in the code to prevent generating harmful content by setting a safety filter.

Prompt Engineering / GenAI

output = model.generate(input_text, [1]=True)

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

Astop

Bmax_length

Ctemperature

Denable_safety_filter

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing safety filter with output length or randomness controls.

Setting enable_safety_filter=True activates content filters to avoid harmful outputs.

4fill in blank

hard

Fill both blanks to set temperature and top_p for safer, more focused output.

Prompt Engineering / GenAI

output = model.generate(input_text, temperature=[1], top_p=[2])

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

A0.2

B0.9

C0.8

D1.5

Attempts:

3 left

💡 Hint

Common Mistakes

Using high temperature or top_p values that increase randomness.

Lower temperature (like 0.2) reduces randomness. Setting top_p to 0.8 limits token choices to the top 80% probability, making output safer and focused.

5fill in blank

hard

Fill all three blanks to add output guardrails: max tokens, stop sequence, and safety filter.

Prompt Engineering / GenAI

output = model.generate(input_text, max_tokens=[1], stop=[2], [3]=True)

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

A'\n'

Benable_safety_filter

C100

Dtemperature

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up parameter names or using wrong types for stop.

Set max_tokens to 100 to limit output length, stop to '\n' to end generation at a newline, and enable_safety_filter=True to prevent harmful content.

Practice

1. What is the main purpose of output guardrails in AI systems?

easy

A. To speed up AI training time

B. To guide AI to give safe and useful answers

C. To increase the size of AI models

D. To reduce the number of AI layers

Solution

1. Step 1: Understand output guardrails

   Output guardrails are rules that help AI give answers that are safe and useful.

2. Step 2: Identify the main goal

   The main goal is to guide AI responses to be helpful and respectful, avoiding harmful or irrelevant content.

3. Final Answer:

   To guide AI to give safe and useful answers -> Option B

4. Quick Check:

   Output guardrails = safe and useful answers [OK]

Hint: Guardrails keep AI answers safe and helpful [OK]

Common Mistakes:

• Confusing guardrails with training speed
• Thinking guardrails increase model size
• Assuming guardrails reduce AI layers

2. Which of the following is a correct example of an output guardrail rule?

easy

A. Block certain harmful words from AI responses

B. Allow AI to generate any length of text without limits

C. Train AI with more data to improve accuracy

D. Increase AI model layers for better output

Solution

1. Step 1: Identify output guardrail examples

   Output guardrails include rules like blocking harmful words or limiting response length.

2. Step 2: Match the correct rule

   Blocking harmful words is a direct guardrail to keep AI responses safe.

3. Final Answer:

   Block certain harmful words from AI responses -> Option A

4. Quick Check:

   Guardrail = block harmful words [OK]

Hint: Guardrails block harmful words, not increase model size [OK]

Common Mistakes:

• Confusing training improvements with guardrails
• Thinking guardrails allow unlimited text
• Mixing model architecture changes with guardrails

3. Given this simple AI output guardrail code snippet in Python:

blocked_words = ['badword']
def filter_output(text):
    for word in blocked_words:
        if word in text:
            return 'Content blocked due to policy.'
    return text

print(filter_output('This is a badword example.'))

What will be the printed output?

medium

A. This is a badword example.

B. Error: blocked_words not defined

C. None

D. Content blocked due to policy.

Solution

1. Step 1: Analyze the filter_output function

   The function checks if any blocked word is in the input text. If found, it returns a block message.

2. Step 2: Check the input text

   The input text contains 'badword', which is in blocked_words, so the function returns the block message.

3. Final Answer:

   Content blocked due to policy. -> Option D

4. Quick Check:

   Blocked word found = block message [OK]

Hint: If blocked word in text, output block message [OK]

Common Mistakes:

• Ignoring the blocked word check
• Assuming original text prints always
• Confusing variable scope errors

4. Consider this Python code meant to limit AI output length:

def limit_length(text, max_len=10):
    if len(text) > max_len:
        return text[:max_len]
    else:
        return text

print(limit_length('Hello, world!'))

What is the output and is there any bug?

medium

A. 'Hello, world!' and no bug

B. Error due to missing return

C. 'Hello, worl' and no bug

D. 'Hello, wor' and no bug

Solution

1. Step 1: Check the function logic

   If text length is more than 10, it returns first 10 characters; else returns full text.

2. Step 2: Apply to input 'Hello, world!'

   Input length is 13, so it returns text[:10] which is 'Hello, worl'.

3. Final Answer:

   'Hello, worl' and no bug -> Option C

4. Quick Check:

   Length limit applied correctly [OK]

Hint: Slice text to max length if too long [OK]

Common Mistakes:

• Counting 11 characters instead of 10
• Assuming no slicing happens
• Thinking code has syntax errors

5. You want to create an output guardrail that blocks any AI response containing both 'error' and 'fail' words, but allows responses with only one of them. Which Python code snippet correctly implements this?

hard

A. def guard(text):
if 'error' in text and 'fail' in text:
return 'Response blocked.'
return text

B. def guard(text):
if 'error' in text or 'fail' in text:
return 'Response blocked.'
return text

C. def guard(text):
if 'error' not in text and 'fail' not in text:
return 'Response blocked.'
return text

D. def guard(text):
if 'error' in text and 'fail' not in text:
return 'Response blocked.'
return text

Solution

1. Step 1: Understand the condition

   The guardrail should block only if both 'error' and 'fail' appear together.

2. Step 2: Check each option logic

   def guard(text): if 'error' in text and 'fail' in text: return 'Response blocked.' return text uses 'and' to check both words, blocking only when both are present, which matches the requirement.

3. Final Answer:

   def guard(text): if 'error' in text and 'fail' in text: return 'Response blocked.' return text -> Option A

4. Quick Check:

   Block if both words present = def guard(text): if 'error' in text and 'fail' in text: return 'Response blocked.' return text [OK]

Hint: Use 'and' to require both words for blocking [OK]

Common Mistakes:

• Using 'or' blocks if either word appears
• Negating conditions incorrectly
• Blocking only one word instead of both