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Prompt Engineering for Everyone Cognitive Class Exam Quiz Answers

Prompt Engineering for Everyone Cognitive Class Certification Answers

Question 1: Can computers inherently understand ambiguous instructions like humans do?

  • Yes
  • No

Question 2: Why do we historically use programming languages instead of plain English to instruct computers?

  • English is easier for computers to understand.
  • English is less ambiguous than programming languages.
  • Programming languages allow for faster execution of tasks.
  • English is more ambiguous than programming languages for providing specific instructions.

Question 3: What does the term ‘zero-shot’ prompting mean in the context of Large Language Models (LLMs)?

  • The model is provided with multiple examples before making a prediction.
  • The model makes a prediction without any prior examples.
  • The model is trained with zero data.
  • The model takes zero seconds to produce an answer.

Question 4: Naive or standard prompts typical use few-shots prompting.

  • True
  • False

Question 5: Is the data that AI models like LLMs are trained on always flawless?

  • Yes, corportations spend billions ensuring such is the case.
  • No, despite best efforts, we can’t escape flawed and biased information.

Question 1: The Naive Approach to prompting the AI often results in overly generic and broad responses.

  • True
  • False

Question 2: Is the Persona Pattern used to make the AI adopt a specific character or identity for more customized results?

  • Yes
  • No

Question 3: Which of the following best describes the “Interview Pattern”?

  • The AI asking the user about its training data.
  • The AI interviewing the user to gather enough specific details for a customized answer.
  • The user providing the AI with all details at once, without the AI asking any questions.
  • The AI asking random questions regardless of the topic.

Question 4: Why would one combine the Persona Pattern with the Interview Pattern?

  • To get more entertaining replies from the AI.
  • To make the AI’s responses impersonal.
  • To get both the viewpoint of an expert character and a detailed answer specific to us.
  • There’s no practical reason to combine them.

Question 5: When requesting the AI to craft a blog post for the “Prompt Engineering for Everyone” course using the Interview Pattern, what did the AI first ask for?

  • The course’s price and duration.
  • Key information about the course, such as target audience and unique selling points.
  • The course’s difficulty level.
  • User reviews and feedback about the course.

Question 1: What were the two phrases mentioned that can be added to the prompt to solicit better answers by doing step-by-step reasoning?

  • “Let’s solve it.” and “Break it down.”
  • “Let’s think step by step.” and “Let’s work this out in a step-by-step way to be sure we have the right answer.”
  • “Solve methodically.” and “Divide and conquer.”
  • “Think deeply.” and “Give a comprehensive answer.”

Question 2: Using the Chain-of-Thought approach always requires retraining the AI model.

  • True
  • False

Question 3: Does using the Zero-Shot CoT prompting technique always produce short answers?

  • Yes
  • No

Question 4: In the provided example about space exploration, why was the Chain-of-Thought approach used?

  • To get a quicker answer.
  • To focus only on the moon landing.
  • To get a more comprehensive and detailed answer by breaking down various facets of the topic.
  • To get a brief summary.

Question 5: What is one downside to using the Chain-of-Thought approach as mentioned in the content?

  • It’s going It requires the AI to be retrained.
  • to make us an offer we can’t refuse.
  • It always provides a concise answer.
  • It may require knowledge of the subject or research, making it time-consuming.

Question 1: According to researchers, the Tree-of-Thought (ToT) approach achieved a 74% success rate in the Game of 24, while Chain-of-Thought only achieved 4%.

  • True
  • False

Question 2: What does the Tree-of-Thought (ToT) prompting encourage the AI to do?

  • Follow a linear sequence of thoughts.
  • Build upon intermediate thoughts and explore branches.
  • Think really hard.
  • Follow a fixed set of instructions.

Question 3: Which of the following can be considered a benefit of the ToT approach?

  • It always gives a concise answer.
  • It provides multiple viewpoints akin to brainstorming.
  • It focuses on a singular expert perspective.
  • It reduces the depth of the answer to make it more generic.

Question 4: What purpose does controlling verbosity serve in the model’s response?

  • To increase the length of every answer.
  • To modify the depth of detail in the response.
  • To improve the accuracy of the answer.
  • To limit the model to short responses only.

Question 5: In the Nova System, who is responsible for ensuring the conversation remains on topic?

  • The Critical Evaluation Expert (CAE).
  • The Critical Execution Expert (CAE)
  • The User.
  • The Discussion Continuity Expert (DCE).

Introduction to Prompt Engineering for Everyone

Prompt engineering is the art and science of crafting effective prompts to elicit desired responses from language models like me! Whether you’re a developer, researcher, or just someone curious about how to get the best results from AI models, understanding prompt engineering can greatly enhance your interaction with these systems.

Here’s a basic introduction to prompt engineering for everyone:

  1. Understanding Language Models: Language models like me are trained on vast amounts of text data and learn to generate text based on the patterns and information present in that data. However, they require prompts—input text—to generate responses. Prompt engineering is about crafting these prompts in a way that guides the model to produce the desired output.
  2. Clarity and Specificity: When crafting a prompt, it’s important to be clear and specific about what you want the model to do. Ambiguous or vague prompts can lead to unexpected or irrelevant responses. Clearly define the task or the information you’re seeking.
  3. Contextual Cues: Providing context within your prompt can help the model better understand the task or question at hand. This can include relevant keywords, phrases, or background information that guide the model’s response.
  4. Examples and Demonstrations: Sometimes, providing examples or demonstrations of the desired output can be more effective than describing it in text. This helps the model understand the desired outcome more clearly.
  5. Iterative Refinement: Prompt engineering often involves an iterative process of trial and error. You may need to experiment with different prompts, adjust parameters, and fine-tune your approach based on the model’s responses.
  6. Domain Knowledge: Understanding the domain or topic you’re working with can greatly inform your prompt engineering efforts. Knowing the vocabulary, terminology, and typical responses within a given field can help you craft more effective prompts.
  7. Evaluation and Validation: Once you’ve crafted a prompt, it’s important to evaluate the model’s response to ensure it meets your criteria for accuracy, relevance, and coherence. This may involve manual inspection or automated metrics, depending on the task.
  8. Ethical Considerations: Finally, it’s essential to consider the ethical implications of the prompts you’re creating. Language models can amplify biases present in the data they’re trained on, so it’s important to be mindful of the language and assumptions embedded in your prompts.

By mastering the art of prompt engineering, you can harness the power of language models more effectively and unlock their full potential for a wide range of tasks and applications.

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