Question answering in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is the main purpose of a question answering system?

Choose the best description of what a question answering system does.

A It predicts numerical values based on historical data.

B It generates answers to questions by understanding and processing natural language input.

C It translates text from one language to another without understanding the meaning.

D It classifies images into categories based on their content.

💡 Hint

Think about what happens when you ask a smart assistant a question.

Question answering systems are designed to understand questions and provide relevant answers by processing natural language.

❓ Predict Output

intermediate

Output of a simple QA model prediction

Given the following code snippet using a pretrained QA model, what will be the printed answer?

Prompt Engineering / GenAI

from transformers import pipeline
qa = pipeline('question-answering')
context = "The Eiffel Tower is located in Paris."
question = "Where is the Eiffel Tower located?"
result = qa(question=question, context=context)
print(result['answer'])

A "located"

B "Eiffel Tower"

C "The Eiffel Tower"

D "Paris"

💡 Hint

The model extracts the answer from the context that best fits the question.

The model finds the phrase in the context that answers the question, which is "Paris".

❓ Model Choice

advanced

Choosing the best model type for open-domain QA

You want to build a system that answers questions about any topic using a large collection of documents. Which model type is best suited?

A A retrieval-augmented generation model that first finds relevant documents then generates answers.

B A simple text classification model that labels questions into categories.

C A convolutional neural network trained on images.

D A clustering algorithm that groups similar questions.

💡 Hint

Think about how to handle large knowledge bases and generate answers.

Retrieval-augmented generation models combine document search with answer generation, ideal for open-domain QA.

❓ Metrics

advanced

Evaluating a QA model with Exact Match and F1 score

A QA model predicted the answer "Paris" for the question "Where is the Eiffel Tower?" The true answer is "Paris, France". What are the Exact Match and F1 scores?

A Exact Match: 0, F1: 0.67

B Exact Match: 1, F1: 1

C Exact Match: 0, F1: 0

D Exact Match: 1, F1: 0.5

💡 Hint

Exact Match requires exact string equality; F1 measures overlap of tokens.

Exact Match is 0 because "Paris" != "Paris, France" exactly. F1 is calculated based on token overlap: tokens in common are "Paris" (1), total tokens predicted (1), total tokens true (2), so F1 = 2*1/(1+2) = 0.67.

🔧 Debug

expert

Why does this QA model code raise an error?

Consider this code snippet:

from transformers import pipeline
qa = pipeline('question-answering')
context = None
question = "What is AI?"
result = qa(question=question, context=context)
print(result['answer'])

Why does it raise an error?

A Because the pipeline is not initialized, it raises an ImportError.

B Because the question is missing, the model raises a ValueError.

C Because the context is None, the model cannot process it and raises a TypeError.

D Because the print statement is incorrect syntax, it raises a SyntaxError.

💡 Hint

Check the type and value of the context variable passed to the model.

The context must be a string. Passing None causes the model to fail with a TypeError because it cannot process a NoneType input.

Practice

1. What is the main purpose of question answering in AI?

easy

A. To find answers from given text or context

B. To generate random text without context

C. To translate languages automatically

D. To create images from descriptions

Solution

1. Step 1: Understand the goal of question answering

   Question answering systems are designed to find specific answers from a given text or context.

2. Step 2: Compare options with the goal

   Only To find answers from given text or context describes finding answers from text, which matches the purpose.

3. Final Answer:

   To find answers from given text or context -> Option A

4. Quick Check:

   Question answering = find answers [OK]

Hint: Focus on 'answer from text' meaning [OK]

Common Mistakes:

• Confusing question answering with translation
• Thinking it generates random text
• Mixing it with image generation

2. Which input is essential for a question answering model to work?

easy

A. Only a context without a question

B. Only a question without any context

C. A question and a related context or passage

D. Random text unrelated to the question

Solution

1. Step 1: Identify inputs needed for question answering

   Question answering requires both a question and some context to find the answer.

2. Step 2: Match options with required inputs

   Only A question and a related context or passage provides both question and related context, which is necessary.

3. Final Answer:

   A question and a related context or passage -> Option C

4. Quick Check:

   Question + context = answer [OK]

Hint: Remember: question needs context to answer [OK]

Common Mistakes:

• Assuming question alone is enough
• Ignoring the need for context
• Choosing unrelated text as input

3. Given this Python code using a question answering model:

from transformers import pipeline
qa = pipeline('question-answering')
context = "The Eiffel Tower is in Paris."
question = "Where is the Eiffel Tower located?"
result = qa(question=question, context=context)
print(result['answer'])

What will be printed?

medium

A. Location unknown

B. Eiffel Tower

C. The Eiffel Tower is in Paris

D. Paris

Solution

1. Step 1: Understand the code's purpose

   The code uses a question answering pipeline to find the answer to the question from the context.

2. Step 2: Identify the answer in the context

   The question asks for location; the context says "The Eiffel Tower is in Paris." So the answer is "Paris".

3. Final Answer:

   Paris -> Option D

4. Quick Check:

   Answer extracted = Paris [OK]

Hint: Look for direct answer in context matching question [OK]

Common Mistakes:

• Printing the whole context instead of answer
• Confusing object with location
• Assuming no answer found

4. This code snippet tries to answer a question but raises an error:

from transformers import pipeline
qa = pipeline('question-answering')
context = "Python is a programming language."
question = "What is Python?"
result = qa(question, context)
print(result['answer'])

What is the error and how to fix it?

medium

A. Error: question is invalid; fix by changing question text

B. Error: missing keyword arguments; fix by using qa(question=question, context=context)

C. Error: context is empty; fix by adding text to context

D. No error; code runs fine

Solution

1. Step 1: Identify the function call error

   The pipeline expects keyword arguments question= and context=, but code passes positional arguments.

2. Step 2: Fix the call with correct keywords

   Change to qa(question=question, context=context) to fix the error.

3. Final Answer:

   Error: missing keyword arguments; fix by using qa(question=question, context=context) -> Option B

4. Quick Check:

   Use keywords for qa() args [OK]

Hint: Use keyword arguments for question and context [OK]

Common Mistakes:

• Passing positional args instead of keywords
• Assuming empty context causes error
• Changing question text unnecessarily

5. You want to build a question answering system that can handle multiple paragraphs and find the best answer. Which approach is best?

hard

A. Split text into paragraphs, run QA on each, then pick highest confidence answer

B. Combine all paragraphs into one string and run QA once

C. Only use the first paragraph for QA

D. Ignore paragraphs and guess answer randomly

Solution

1. Step 1: Understand handling multiple paragraphs

   QA models usually work best on smaller text chunks, so splitting helps.

2. Step 2: Choose method to find best answer

   Running QA on each paragraph separately and selecting the answer with highest confidence ensures accuracy.

3. Final Answer:

   Split text into paragraphs, run QA on each, then pick highest confidence answer -> Option A

4. Quick Check:

   Split + score answers = best result [OK]

Hint: Split text, run QA per part, pick best answer [OK]

Common Mistakes:

• Running QA on all text at once causing confusion
• Ignoring paragraphs reduces accuracy
• Guessing answers without context