Hallucination detection in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is hallucination in AI-generated text?

In the context of AI language models, what does the term 'hallucination' mean?

A The model repeats the same sentence multiple times in the output.

B The model produces text that is grammatically incorrect but factually accurate.

C The model generates text that is factually incorrect or unsupported by data.

D The model refuses to generate any output due to lack of data.

💡 Hint

Think about when AI makes up information that isn't true.

Hallucination in AI means the model creates information that looks plausible but is actually false or unsupported.

❓ Predict Output

intermediate

Detecting hallucination with confidence scores

Given a model output with confidence scores for each token, which output indicates a higher chance of hallucination?

Prompt Engineering / GenAI

tokens = ['The', 'capital', 'of', 'France', 'is', 'Berlin']
confidences = [0.99, 0.98, 0.97, 0.96, 0.95, 0.40]

# Which token likely indicates hallucination?

A The token 'The' with confidence 0.99

B The token 'Berlin' with confidence 0.40

C The token 'capital' with confidence 0.98

D The token 'France' with confidence 0.96

💡 Hint

Lower confidence tokens may indicate hallucination.

Lower confidence on a token, especially when it contradicts known facts, suggests hallucination.

❓ Model Choice

advanced

Choosing a model architecture to reduce hallucination

Which model architecture is best suited to reduce hallucination in generated text by grounding outputs on external knowledge?

A A retrieval-augmented generation model that uses external databases during generation

B A convolutional neural network trained on images

C A standard transformer language model trained only on text data

D A recurrent neural network trained on random noise

💡 Hint

Models that check facts during generation help reduce hallucination.

Retrieval-augmented models access external knowledge to verify facts, reducing hallucination.

❓ Metrics

advanced

Evaluating hallucination with automatic metrics

Which automatic metric is most appropriate to measure hallucination in AI-generated summaries?

A FactCC score measuring factual consistency between summary and source document

B BLEU score comparing generated text to reference text

C ROUGE score comparing generated summary to reference summary

D Perplexity measuring how well the model predicts the next word

💡 Hint

Look for metrics that check factual correctness, not just similarity.

FactCC is designed to detect factual inconsistencies, making it suitable for hallucination evaluation.

🔧 Debug

expert

Identifying hallucination cause in model output

You have a language model that often hallucinates facts in its answers. Which debugging step is most likely to help reduce hallucination?

A Remove all stop words from the training data

B Increase the model size without changing training data

C Reduce the learning rate to zero during training

D Fine-tune the model on a high-quality, fact-checked dataset

💡 Hint

Improving training data quality helps reduce hallucination.

Fine-tuning on fact-checked data helps the model learn accurate information and reduces hallucination.

Practice

1. What is the main goal of hallucination detection in AI models?

easy

A. To improve the speed of AI responses

B. To find when AI says things that are not true

C. To increase the size of AI training data

D. To reduce the cost of running AI models

Solution

1. Step 1: Understand the term 'hallucination' in AI context

   Hallucination means AI generates false or made-up information.

2. Step 2: Identify the purpose of detection

   Hallucination detection aims to find these false outputs to improve trust.

3. Final Answer:

   To find when AI says things that are not true -> Option B

4. Quick Check:

   Hallucination detection = find false AI outputs [OK]

Hint: Hallucination means false info; detection finds it [OK]

Common Mistakes:

• Confusing hallucination with model speed or size
• Thinking it improves training data
• Assuming it reduces cost directly

2. Which of the following is a correct simple method to detect hallucination in AI output?

easy

A. Compare AI output with trusted information using similarity scores

B. Increase the AI model size to reduce hallucination

C. Train AI with random noise data

D. Ignore output and only check input data

Solution

1. Step 1: Recall simple hallucination detection methods

   Simple methods compare AI output to trusted facts using similarity measures.

2. Step 2: Evaluate options

   Only Compare AI output with trusted information using similarity scores describes this correct approach; others are unrelated or incorrect.

3. Final Answer:

   Compare AI output with trusted information using similarity scores -> Option A

4. Quick Check:

   Simple detection = compare output to facts [OK]

Hint: Check AI output against trusted info for quick detection [OK]

Common Mistakes:

• Thinking bigger models reduce hallucination automatically
• Using random noise data for training
• Ignoring output in detection

3. Given this Python code snippet for hallucination detection, what is the output?

trusted_facts = ['Paris is the capital of France']
ai_output = 'Paris is the capital of France'

similarity_score = 1.0 if ai_output in trusted_facts else 0.0
print(similarity_score)

medium

A. 1.0

B. 0.0

C. Error

D. None

Solution

1. Step 1: Check if AI output matches trusted facts

   The string 'Paris is the capital of France' is exactly in the trusted_facts list.

2. Step 2: Determine similarity score

   Since the output is found, similarity_score is set to 1.0 and printed.

3. Final Answer:

   1.0 -> Option A

4. Quick Check:

   Output matches fact = 1.0 [OK]

Hint: If output in facts, similarity = 1.0 [OK]

Common Mistakes:

• Confusing list membership with substring check
• Expecting 0.0 if exact match
• Thinking code raises error

4. Find the error in this hallucination detection code snippet:

trusted_facts = ['Water boils at 100 degrees Celsius']
ai_output = 'Water boils at 90 degrees Celsius'

if ai_output == trusted_facts:
    print('No hallucination')
else:
    print('Possible hallucination')

medium

A. ai_output should be a list, not string

B. Missing import statement for list

C. Comparing string to list directly causes wrong result

D. Syntax error in if statement

Solution

1. Step 1: Analyze the comparison in if statement

   The code compares a string (ai_output) to a list (trusted_facts) using ==, which is always False.

2. Step 2: Understand impact on hallucination detection

   This causes the code to always print 'Possible hallucination' even if output matches a fact.

3. Final Answer:

   Comparing string to list directly causes wrong result -> Option C

4. Quick Check:

   String == list comparison is incorrect [OK]

Hint: Compare string to string, not string to list [OK]

Common Mistakes:

• Thinking syntax error exists
• Assuming ai_output must be list
• Missing import statements

5. You want to detect hallucinations in AI-generated medical advice. Which approach best combines accuracy and reliability?

hard

A. Trust AI output without verification to save time

B. Only check if AI output length is less than 100 characters

C. Randomly accept or reject AI output

D. Use advanced fact-checking models comparing AI output to verified medical databases

Solution

1. Step 1: Consider the importance of accuracy in medical advice

   Medical advice must be accurate and reliable to avoid harm.

2. Step 2: Evaluate detection methods

   Advanced fact-checking against verified databases ensures correctness and reduces hallucination risk.

3. Step 3: Reject unreliable or random methods

   Ignoring verification or random acceptance risks dangerous errors.

4. Final Answer:

   Use advanced fact-checking models comparing AI output to verified medical databases -> Option D

5. Quick Check:

   Fact-checking with trusted data = best for medical AI [OK]

Hint: Use trusted databases for fact-checking medical AI output [OK]

Common Mistakes:

• Ignoring verification for speed
• Using output length as accuracy measure
• Random acceptance of AI output