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Prompt engineering: Difference between revisions
→Prompt Engineering for Code Generation Models
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Give the coding model a high-level task description. To improve the quality of the generated code, it's recommended to start the prompt with a broad description of the task at hand. For example, if you want to generate Python code to plot data from a standard [[dataset]], you can provide a prompt like this: | Give the coding model a high-level task description. To improve the quality of the generated code, it's recommended to start the prompt with a broad description of the task at hand. For example, if you want to generate Python code to plot data from a standard [[dataset]], you can provide a prompt like this: | ||
<code># Load iris data from scikit-learn datasets and plot the training data.</ | <code># Load iris data from scikit-learn datasets and plot the training data.</code> | ||
However, sometimes the generated code may not be optimal, in which case you can provide more specific instructions such as importing libraries before using them. By combining a high-level task description with detailed user instructions, you can create a more effective prompt for coding model to generate code. | However, sometimes the generated code may not be optimal, in which case you can provide more specific instructions such as importing libraries before using them. By combining a high-level task description with detailed user instructions, you can create a more effective prompt for coding model to generate code. | ||