GPT-5 Prompting Techniques

Overview

This episode of the AI Daily Brief reviews the early community and OpenAI guidance on effective prompting techniques for GPT-5, highlighting a renewed emphasis on structured, explicit instructions and prompt engineering to maximize model performance.

Current State of GPT-5 Adoption

• Initial complaints around GPT-5 have been partly addressed, including model availability and user controls.
• The community remains divided on the model’s overall value and best practices.
• OpenAI and other experts have released prompting guides reflecting the need for new approaches.

Foundational Prompting Techniques for GPT-5

• The model is highly steerable, requiring clear, explicit prompts for optimal output.
• Explicitly instructing GPT-5 to "think harder" or "ultrathink" can improve reasoning and depth.
• Using detailed, structured prompts—including planning phases and explicit reasoning steps—ensures more comprehensive responses.
• JSON or similarly structured formatting increases output predictability, but the format itself matters less than the detail and structure provided.
• Prompts should clearly define expectations for style, tone, and output format.

Additional Prompting Best Practices

• Request the model to explain its thought process to improve transparency and performance on complex tasks.
• Avoid conflicting or ambiguous instructions, as GPT-5 expends resources trying to resolve contradictions, potentially reducing output quality.
• Leverage the model’s capacity for iteration and self-evaluation by asking it to create and iterate against custom rubrics.
• Employ metaprompting: ask GPT-5 to optimize, evaluate, or improve prompts based on user goals or known shortcomings.

Agentic and Advanced Controls

• New API parameters allow control over reasoning effort (depth of thinking) and verbosity (answer length).
• GPT-5 supports parallel processing of independent tasks when instructed; include constraints if certain outputs are interdependent.

Key Takeaways for Prompt Engineering with GPT-5

• Structure and explicitness in prompts are more crucial than with previous models.
• Iterative prompting, granular feedback, and “prompting the model to improve prompts” are highly effective.
• Mindset shift: prompt engineering skills are necessary again for advanced use.
• OpenAI's prompt optimizer tool and guides provide concrete templates and rationales for improving prompt effectiveness.