Question answering in Prompt Engineering / GenAI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is Question Answering (QA) in AI?

Question Answering is a task where a computer program reads a question and finds the best answer from a given text or knowledge source, just like how you ask a friend and they reply with the right information.

beginner

What are the two main types of Question Answering systems?

The two main types are: 1) Closed-domain QA, which answers questions about a specific topic, and 2) Open-domain QA, which can answer questions about any topic using large knowledge sources.

intermediate

How does a machine learning model find answers in text for QA?

The model reads the question and the text, then learns to spot the exact part of the text that answers the question, similar to how you scan a book to find a sentence that answers your question.

beginner

What is the role of 'context' in Question Answering?

Context is the text or information where the answer is found. The QA system uses this context to understand the question better and find the right answer inside it.

intermediate

Name a popular dataset used to train and test Question Answering models.

SQuAD (Stanford Question Answering Dataset) is a popular dataset where models learn to answer questions by reading paragraphs from Wikipedia.

What does a Question Answering system primarily do?

A Detects objects in images

B Generates random text

C Translates languages

D Finds answers to questions from text or knowledge

QA systems focus on finding answers to questions using text or knowledge sources.

Which type of QA system answers questions about any topic?

A Open-domain QA

B Closed-domain QA

C Image QA

D Speech QA

Open-domain QA can answer questions on any topic using large knowledge bases.

In QA, what is 'context'?

A The question itself

B The text where the answer is found

C The model architecture

D The answer only

Context is the text or information where the answer is located.

Which dataset is commonly used to train QA models?

A MNIST

B ImageNet

C SQuAD

D COCO

SQuAD is a popular dataset for training and testing QA models.

How does a QA model find the answer in the text?

A By scanning the text to locate the answer span

B By translating the question

C By guessing randomly

D By summarizing the text

QA models scan the text to find the exact part that answers the question.

Explain what Question Answering is and how it works in simple terms.

Think about how you ask a friend a question and they find the answer in a book.

Describe the difference between closed-domain and open-domain Question Answering systems.

One is about specific topics, the other covers any topic.

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