Chatbot development basics in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Intent Recognition in Chatbots

Which of the following best describes the role of intent recognition in a chatbot?

A It translates user messages into multiple languages.

B It generates the chatbot's response text based on user input.

C It stores the conversation history for future reference.

D It identifies the user's goal or purpose behind their message.

💡 Hint

Think about what the chatbot needs to understand before replying.

Intent recognition helps the chatbot understand what the user wants to achieve, which guides the response.

❓ Predict Output

intermediate

Output of Simple Rule-Based Chatbot Code

What will be the output of the following Python code snippet?

Prompt Engineering / GenAI

def chatbot_response(message):
    if 'hello' in message.lower():
        return 'Hi there! How can I help you?'
    else:
        return 'Sorry, I did not understand.'

print(chatbot_response('Hello, bot!'))

A hello

B Hi there! How can I help you?

C Sorry, I did not understand.

D None

💡 Hint

Check if the word 'hello' is detected in the input message.

The code checks if 'hello' is in the message ignoring case, so it returns the greeting response.

❓ Hyperparameter

advanced

Choosing Embedding Size for Chatbot NLP Model

When training a chatbot's natural language understanding model, which embedding size is generally best for balancing performance and speed?

A 5 dimensions

B 5000 dimensions

C 50 dimensions

D 10000 dimensions

💡 Hint

Too small loses meaning, too large slows training.

Embedding sizes around 50 to 300 dimensions usually balance capturing meaning and computational efficiency.

❓ Metrics

advanced

Evaluating Chatbot Response Quality

Which metric is most appropriate to measure how well a chatbot's responses match expected answers in a classification task?

A Accuracy

B Mean Squared Error

C BLEU Score

D Root Mean Squared Error

💡 Hint

Think about classification vs. text similarity metrics.

Accuracy measures the percentage of correct predicted classes, suitable for intent classification tasks.

🔧 Debug

expert

Debugging Chatbot Response Generation Code

What error will this Python code raise when run?

Prompt Engineering / GenAI

def generate_response(user_input):
    responses = {'hi': 'Hello!', 'bye': 'Goodbye!'}
    return responses[user_input]

print(generate_response('hello'))

A KeyError

B TypeError

C SyntaxError

D IndexError

💡 Hint

Check if the key 'hello' exists in the dictionary.

The dictionary does not have the key 'hello', so accessing it raises a KeyError.