Why Question answering in Prompt Engineering / GenAI? - Purpose & Use Cases

The Big Idea

What if you could get any answer you want instantly, without digging through piles of information?

The Scenario

Imagine you have a huge book and someone asks you a specific question about its content. You try to find the answer by flipping through pages one by one, reading paragraphs, and hoping to spot the right information quickly.

The Problem

This manual search is slow and tiring. You might miss the answer, get confused by too much information, or take a long time to respond. It's easy to make mistakes or give incomplete answers when you rely only on memory or slow reading.

The Solution

Question answering systems use smart models to quickly understand the question and find the exact answer from large texts or databases. They save time, reduce errors, and provide clear, precise answers instantly.

Before vs After

✗ Before

Read book page by page;
Look for keywords;
Guess answer;

✓ After

answer = model.answer(question, text)

What It Enables

It enables instant, accurate answers from vast information, making knowledge accessible anytime.

Real Life Example

Customer support chatbots that instantly answer your questions about products or services without waiting on hold.

Key Takeaways

Manual searching for answers is slow and error-prone.

Question answering models quickly find precise answers.

This makes information easy and fast to access.

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