GPT-5 Prompt Optimization Guide

GPT-5 Capabilities and Usage - GPT-5 excels in tool calling, instruction following, and long context understanding, making it suitable for agentic use cases, especially for developers [3][4] - The model's "reasoning effort" can be adjusted to control its thoroughness and efficiency, impacting token usage and cost [7][8][9] - Users can define clear criteria in prompts to guide the model's exploration of the problem space, including context gathering strategies and early stop conditions [9][10][11][12] - Tool preambles provide real-time updates on the model's activities, enhancing transparency and control [22][23][24] - The Responses API is recommended over Chat Completions due to statistically significant improvements in evaluations, improved agentic flows, lower costs, and more efficient token usage [27][28] Prompt Engineering and Optimization - For coding tasks, especially front-end development, GPT-5 performs best with popular languages and frameworks like Nextjs, TypeScript, React, and Tailwind CSS [30][31][32] - The model can be instructed to create a self-constructed rubric to measure its performance, improving output quality in one-shot web application development [33][34][35][36] - Defining code editing rules and guiding principles in the prompt helps the model adhere to existing codebase patterns and design standards [39][40] - GPT-5's verbosity can be controlled to influence the length of the final answer, while reasoning effort controls the length of its thinking process [47] - The prompt optimizer tool in the playground allows users to refine prompts with direct feedback and explanations [60][61][62][63][64][65]