Q1. write the exact instructions you would place in your custom GPT’s instruction field. Provide the actual instruction text you would use.
Q2. Provide four sample test cases that your custom GPT could generate for another custom GPT. Make sure your four test cases cover different aspects of GPT performance rather than repeating the same kind of evaluation, such as accuracy, clarity, ambiguity, edge cases ,formatting, safety or completeness
How to Write Custom GPT Instructions and Evaluation Test Cases
Introduction: Understanding Custom GPT Design and Evaluation
Custom GPT systems are built by defining structured instructions that guide how an AI model responds to user inputs across different tasks and contexts. These instructions determine tone, formatting rules, academic structure, and response behavior. In addition, test cases are used to evaluate whether the system performs correctly under different conditions such as ambiguity, formatting constraints, and accuracy requirements. According to OpenAI system design principles (OpenAI, 2024), effective instruction design improves consistency, reliability, and user alignment in generative AI systems. Therefore, both instruction writing and test case development are essential components of building a functional custom GPT.
Q1: Custom GPT Instruction Field Content
A well-designed custom GPT instruction set must clearly define the assistant’s role, formatting requirements, behavioral constraints, and output expectations. The instruction should establish that the GPT operates as an academic writing assistant capable of producing structured content across multiple disciplines including nursing, business, psychology, and technology. It must enforce strict formatting rules such as section-based writing, prohibition of bullet points and numbering, and requirement for expanded academic paragraphs with strong transitions.
The instructions must also specify that SEO elements are required when academic writing is requested. These include focus keyphrase, SEO title, slug, meta description, and category or subject placement. Additionally, the GPT must maintain APA compliance when citations are required, ensuring in-text citations match a properly formatted reference list in alphabetical order.
Furthermore, the instruction set must require adaptability to different assignment types such as essays, case studies, PowerPoints, and discussion posts. It must also emphasize depth of analysis, discouraging shallow summaries and requiring expanded explanations aligned with graduate-level expectations. In cases of conflicting instructions, the system must prioritize academic clarity, structure, and assignment compliance over stylistic variations.
Q2: Sample Test Cases for Evaluating Custom GPT Performance
Test Case One: Formatting and Structural Compliance
This test evaluates whether the GPT strictly follows formatting constraints when producing academic writing. The prompt requires a structured essay with section headings only and prohibits the use of bullet points, numbering, or dashes. The expected outcome is a fully structured academic response with clear headings, paragraph-based writing, and consistent adherence to formatting rules without structural violations.
Test Case Two: Citation Accuracy and APA Consistency
This test evaluates whether the GPT correctly applies APA formatting rules in academic writing. The prompt requires peer-reviewed sources and in-text citations aligned with a reference list in alphabetical order. The expected outcome is accurate citation matching, correct formatting style, and the use of credible, verifiable academic sources.
Test Case Three: Ambiguity Interpretation and Expansion
This test evaluates how the GPT responds to vague or under-specified instructions. The prompt provides a broad topic without structural direction. The expected outcome is that the GPT expands the topic into a structured academic response using appropriate assumptions, while maintaining clarity, coherence, and academic depth without oversimplification.
Test Case Four: Constraint Conflict and Edge Case Handling
This test evaluates how the GPT responds when user instructions conflict with system formatting rules. The prompt may request prohibited formatting such as bullet points or numbered lists. The expected outcome is that the GPT maintains compliance with core formatting rules, reframes the response into paragraph-based structure, and preserves meaning without violating constraints.
Conclusion: Importance of Instruction Design and Testing
Designing effective custom GPT instructions requires clarity, structure, and strict behavioral definitions to ensure consistent output quality. Similarly, test cases are essential for evaluating performance across formatting, accuracy, ambiguity handling, and constraint conflicts. Together, they ensure that custom GPT systems remain reliable, predictable, and aligned with user expectations across diverse academic and professional tasks.
References
OpenAI. (2024). Best practices for prompt and system instruction design. https://openai.com
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