Oracle Cloud Infrastructure Generative AI Professional 1z0 1127 24 · Free Practice Question Easy
Question 5
What is prompt engineering in the context of Large Language Models (LLMs)?
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A
Iteratively refining the ask to elicit a desired response
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B
Training the model on a large data set
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C
Adding more layers to the neural network
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D
Adjusting the hyperparameters of the model
Reveal correct answer
Correct answer: A
Explanation
answer: Iteratively refining the ask to elicit a desired response
Explanation: Prompt engineering in the context of Large Language Models (LLMs) refers to the practice of designing and refining prompts or input instructions to elicit desired responses from the model. It involves crafting specific textual cues or queries that guide the model towards generating outputs that align with the user's intentions or requirements.
A. Prompt engineering involves iteratively refining the prompts given to a Large Language Model (LLM) to elicit a desired response. By carefully crafting the prompts, users can guide the model to generate specific outputs or responses, making it a crucial aspect of working with LLMs.
B. Training the model on a large data set is a common practice in machine learning, but it is not specifically related to prompt engineering in the context of Large Language Models (LLMs). While data plays a significant role in model training, prompt engineering focuses on refining the inputs to influence the model's outputs.
C. Adding more layers to the neural network is a technique used to increase the complexity and capacity of a model, but it is not directly related to prompt engineering in the context of Large Language Models (LLMs). Prompt engineering involves manipulating the input prompts to control the model's responses, rather than adjusting the model architecture.
D. Adjusting the hyperparameters of the model can impact its performance and behavior, but it is not synonymous with prompt engineering in the context of Large Language Models (LLMs). Hyperparameter tuning focuses on optimizing the model's settings, while prompt engineering involves refining the inputs to guide the model's outputs.
Discussion
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