Prompt Engineering for Custom GPTs: How to Optimize Your AI Instructions

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When moving to custom GPTs , implementing effective prompt engineering practices in your instructions is important to ensure that your GPTs work reliably and accurately. OpenAI has published a dedicated guide for this , from which this article was created. Here is a compact guide to help you get the most out of your GPTs.

1. Optimization of instructions
1.1 Simplifying complex instructions
An important aspect of creating instructions for Custom GPTs is simplifying complex steps. Break multi-step instructions into simpler, more manageable steps so that the model can follow them accurately. Use "trigger/instruction pairs" separated by commas to improve the reliability of following the steps without merging or skipping steps.

Prompt Engineering for Custom GPTs: How to Optimize Your AI Instructions 1
1.2 Structuring for more clarity
Break down second-level instructions into separate steps to ensure better execution. Use separators between sets of instructions and to highlight few-shot examples to increase clarity. A clear structure makes it easier for the model to follow the instructions precisely.

1.3 Promoting attention to detail
Incorporate commands such as "take your time," "take a deep breath," and "check your work" into your instructions to encourage the model to work thoroughly. Use "reinforcing language" to emphasize algeria phone number data critical parts of the instructions and ensure they are not missed. These approaches encourage attention to detail and improve the accuracy of results.

1.4 Avoiding negative instructions
To improve compliance and avoid confusion, phrase your instructions in a positive way. Negative instructions can confuse the model and lead to undesirable results. Instead, focus on communicating clearly what you expect.

1.5 Granular Steps
Break down the steps into as much detail as possible, especially if multiple actions are required within a single step. The more granular the instructions, the better the model can understand and execute them. Avoid bundling too many actions into one step.

1.6 Consistency and clarity
Explicitly define terms and expectations you use using few-shot prompting (e.g., acceptable vs. unacceptable changes) to improve consistency in assessments. Clarify all relevant classifications using few-shot examples to reduce variability in output. Consistency and clarity are critical for reliable results.