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Good luck. 🤞
1. What is the first principle of writing an effective prompt?
A) Write long and complex instructions to give the model as much detail as possible. B) Write clear and specific instructions to guide the model's output effectively. C) Use creative and vague language to allow the model to interpret freely. D) Avoid giving explicit instructions so the model can use pre-trained intuition.
2. What is the benefit of using Chain-of-Thought (CoT) reasoning in prompt engineering?
A) It provides multiple examples in the prompt, allowing the model to follow the general logic without specific instructions. B) CoT allows the model to mimic human-like step-by-step reasoning, improving accuracy by showing the intermediate steps to a solution. C) CoT helps the model avoid overthinking and focus only on the final answer, skipping intermediate steps. D) It reduces prompt length by summarizing complex problems into one sentence.
3. How can recency bias be mitigated in language models?
A) Randomize the order of examples within the prompt or finish with a balanced number of categories. B) Always place important examples at the end of the prompt to ensure they are considered. C) Provide fewer examples to avoid bias, focusing on a single type of example in the prompt. ( D) Use zero-shot prompts to reset the model’s attention span.
4. How does few-shot prompting improve model performance?
A) By providing the model with multiple examples within the prompt, guiding it towards the desired output. B) By giving no examples at all, which forces the model to generate the output from scratch. C) By providing one incorrect example so the model can understand what not to do. D) By showing the model one prompt with multiple completions for better temperature control.
5. Which type of reasoning involves drawing a specific conclusion from general premises or assumptions?
A) Inductive reasoning B) Deductive reasoning C) Analogical reasoning D) Divergent reasoning
6. What is one key strategy to mitigate majority label bias in prompts?
A) Always provide only the dominant class to reduce confusion. B) Place the dominant class first in the prompt to highlight its importance. C) Use fewer examples overall to reduce bias in the model. D) Include a balanced number of examples from each class to minimize bias.
7. Why is specifying task constraints like “MANDATORILY” or using capital letters in prompts useful?
A) It increases the temperature and top-p values of the response model. B) It makes the prompt more human-readable for later editing. C) It helps reduce hallucinations by signaling importance to the model. D) It emphasizes instructions, helping models avoid task degradation.
8. What does “Chain-of-Thought” prompting improve most significantly in model outputs?
A) Style matching and creativity. B) Shorter outputs with fewer tokens. C) Logical progression and multi-step reasoning. D) Token diversity through presence penalty.
9. Which of the following best describes inductive reasoning in language models?
A) Using general assumptions to deduce specific facts. B) Making a guess based on partial input with no prior pattern. C) Comparing two things based on similarity. D) Generalizing from specific examples to probable conclusions
10. Why is it important to test prompts using real-world inputs?
A) Because synthetic prompts are blocked by most LLMs. B) To observe how the model performs on perfect edge cases. C) To ensure prompt outputs align with the real data variability users provide. D) Because prompts are only reliable if they’re under 50 tokens.
