OpenAI’s developer team has shared a detailed prompting guide for the newly launched GPT-5. The team has shared some tips to get the most out of its coding and agentic capabilities, especially for frontend and software engineering workflows.
The guide focuses on helping developers fine-tune how GPT-5 approaches tool calling, follows instructions, and manages long-context tasks. It includes strategies for controlling the model’s “agentic eagerness”, whether you want GPT-5 to act quickly with minimal exploration or operate more autonomously with deeper reasoning. Developers can also use new API parameters like reasoning_effort and verbosity to adjust how thoroughly the model thinks and how much it writes.
For coding, OpenAI highlights GPT-5’s strength in large-scale projects, from building entire apps in one go to performing complex multi-file refactors. The guide also shares frontend best practices, recommending stacks like Next.js with TypeScript, Tailwind CSS, and shadcn/ui for optimal results. AI code editor Cursor’s early testing with GPT-5 is featured as a case study, showing how prompt tuning improved code clarity, reduced unnecessary tool calls, and increased autonomy in long tasks.
Apart from that, the guide suggests breaking up complex jobs into smaller tasks, using tool preambles for clearer progress updates, and using the Responses API to maintain reasoning context between calls, a move that, according to OpenAI, has already boosted performance metrics in early evaluations.
While the document is aimed at technical users, it reinforces a broader point: GPT-5 is highly steerable but benefits most from precise, conflict-free instructions. This aligns with recent remarks from OpenAI CEO Sam Altman, who noted that some people are forming deeper attachments to specific AI models, and that suddenly removing older ones was “a mistake.” He also cautioned against using the technology in ways that blur the line between reality and fiction.
With this guide, OpenAI seems intent on not only showing off GPT-5’s technical muscle but also ensuring developers have a clear playbook for integrating it into production environments, from fast prototypes to long-running, complex systems.
