What do you know about ChatGPT?

#1. How can prompt engineering address ambiguity in language models?

By avoiding ambiguous language in prompts

By allowing models to guess ambiguous intentions

By refining prompts to provide clearer context

By ignoring ambiguous prompts

#2. What is the primary purpose of ChatGPT?

To analyze images

To transform natural language

To play video games

To compose music

#3. What is a key advantage of transfer learning in the context of ChatGPT?

It reduces the model’s size

It enhances model adaptability to different tasks

It improves model accuracy on a single task

It speeds up the training process

#4. How can ChatGPT contribute to streamlining workflows?

By managing financial transactions

By automating routine administrative tasks

By designing user interfaces

By optimizing website performance

#5. How can ChatGPT be employed to generate creative ideas?

By strictly following predefined templates

By restricting input prompts to factual information

By encouraging playful and open-ended prompts

By avoiding prompts that involve imagination

#6. What is the primary purpose of OpenAI’s Playground?

To train new language models

To visualize and interact with ChatGPT in real-time

To restrict access to ChatGPT outputs

To limit experimentation with language models

#7. In which industry might ChatGPT be applied to automate routine administrative tasks?

Manufacturing

Information Technology

Finance

Healthcare

#8. How can prompt engineering contribute to creating engaging chatbot responses?

By using only pre-defined templates

By incorporating humor and personality

By avoiding interactive elements

By restricting responses to factual information

#9. What is a consideration when deploying ChatGPT-powered applications in real-world environments?

Ignoring user feedback

Frequent model fine-tuning

Lack of scalability planning

Encouraging user experimentation

#10. What is an important consideration when exploring and addressing bias in language models?

Ignoring the impact of bias on model outputs

Acknowledging and actively addressing bias

Amplifying biased responses for diversity

Prioritizing biases that align with user preferences