Prompt Engineering Quiz

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What is the primary goal of prompt engineering in the context of large language models? Fine-tuning the model parameters Designing prompts to elicit desired responses Training a new model from scratch Adjusting the learning rate

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Which of the following is crucial for designing effective prompts? Model architecture Length of the prompt Clear instructions and context Dataset size

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Which of the following can improve the quality of generated responses in prompt engineering? Using ambiguous language Reducing model parameters Providing specific examples in the prompt Randomizing the prompt

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What does "few-shot learning" in prompt engineering mean? Providing a few examples of the desired output Training the model on a large dataset Testing the model with different architectures Limiting the number of generated outputs

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What type of prompt should be used to elicit longer and more descriptive answers? Command-based Closed-ended Open-ended Conditional

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What is a key challenge in designing prompts for large language models? Training time Handling model drift Avoiding bias in the responses Hardware limitations

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Which of the following is an example of a prompt template? "Explain why [topic] is important." "Train a new model." "Calculate the response time." "Close the session."

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What technique can help avoid ambiguity in prompt design? Using complex vocabulary Adding constraints and context Reducing input size Simplifying the model

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How does token length affect prompt performance in models like GPT? It doesn't affect performance Longer prompts always lead to better responses Exceeding token limits can truncate the prompt or response Shorter prompts reduce processing speed

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What is a recommended approach to mitigate model hallucinations in prompt engineering? Use more training data Reduce the number of tokens in the prompt Randomize the prompt structure Include retrieval-based systems or external knowledge

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What does "chain-of-thought prompting" aim to achieve? Forcing the model to use a predefined list of vocabulary Encouraging the model to generate step-by-step reasoning Limiting the model's response length Speeding up the token generation process

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Which method can be used to influence a model's output toward specific styles or formats? Adjusting the model architecture Using style-transfer training Incorporating format-specific instructions in the prompt Fine-tuning the dataset

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Which technique is useful for improving task-specific performance in prompt engineering without retraining the model? Instruction tuning Data augmentation Transfer learning Zero-shot learning

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How does prompt concatenation work to improve responses in large language models? Appends multiple smaller prompts into one Merges multiple responses for better accuracy Combines prompts with external data sources Modifies the model parameters based on the prompt

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What does "prompt tokenization" refer to in the context of large language models? Assigning weights to different parts of the prompt Converting prompt text into token sequences for processing Splitting the prompt into multiple smaller prompts Compressing the prompt for faster processing

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Prompt Engineering Quiz

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