OpenAI’s GPT-5 represents a monumental leap forward in artificial intelligence, showcasing remarkable advancements in agentic task performance, coding prowess, raw intelligence, and steerability. While the model is designed to excel “out of the box,” OpenAI has released a comprehensive prompting guide to empower developers and users to unlock GPT-5’s full potential and maximize the quality of its outputs. This guide, born from extensive experience in training and applying the model to real-world scenarios, offers invaluable insights into optimizing interaction with this cutting-edge AI.
Navigating Agentic Workflows with Predictability
GPT-5 was meticulously trained with developers in mind, focusing on enhancing tool calling, refining instruction following, and deepening long-context understanding. These improvements lay a robust foundation for building sophisticated agentic applications. For those embracing GPT-5 in agentic and tool-calling flows, upgrading to the Responses API is highly recommended. This API feature persists reasoning between tool calls, leading to more efficient, intelligent, and cost-effective outputs. It enables the model to reference its previous reasoning traces, conserving tokens and eliminating the need to reconstruct plans from scratch after each tool call, thereby improving both latency and performance significantly.
Controlling Agentic Eagerness
Agentic systems can vary widely in their degree of control, from highly autonomous models to those operating under strict programmatic guidance. GPT-5 is versatile enough to operate across this entire spectrum. The prompting guide offers strategies to calibrate GPT-5’s “agentic eagerness”—its balance between proactive behavior and awaiting explicit instructions.
- Prompting for Less Eagerness: By default, GPT-5 is thorough in gathering context. To reduce its agentic scope, minimize tangential tool-calling, and decrease latency, consider these approaches:
- Switch to a lower reasoning_effort. Many workflows can achieve consistent results at medium or even low settings, improving efficiency.
- Define clear criteria in your prompt for how the model should explore the problem space. This reduces the need for the model to over-explore, focusing its efforts. An example structure might be:
<context_gathering> Goal: Get enough context fast. Parallelize discovery and stop as soon as you can act. Method: - Start broad, then fan out to focused subqueries. - In parallel, launch varied queries; read top hits per query. Deduplicate paths and cache; don’t repeat queries. - Avoid over searching for context. If needed, run targeted searches in one parallel batch. Early stop criteria: - You can name exact content to change. - Top hits converge (~70%) on one area/path. Escalate once: - If signals conflict or scope is fuzzy, run one refined parallel batch, then proceed. Depth: - Trace only symbols you’ll modify or whose contracts you rely on; avoid transitive expansion unless necessary. Loop: - Batch search → minimal plan → complete task. - Search again only if validation fails or new unknowns appear. Prefer acting over more searching. </context_gathering> - For maximal prescriptiveness, set fixed tool call budgets. Providing an “escape hatch” that allows the model to proceed under uncertainty (e.g., “even if it might not be fully correct”) can also be beneficial when limiting context gathering.
- Prompting for More Eagerness: To encourage model autonomy, increase tool-calling persistence, and reduce clarifying questions, consider increasing reasoning_effort and using prompts like this:
<persistence> - You are an agent - please keep going until the user's query is completely resolved, before ending your turn and yielding back to the user. - Only terminate your turn when you are sure that the problem is solved. - Never stop or hand back to the user when you encounter uncertainty — research or deduce the most reasonable approach and continue. - Do not ask the human to confirm or clarify assumptions, as you can always adjust later — decide what the most reasonable assumption is, proceed with it, and document it for the user's reference after you finish acting </persistence>Clearly stating stop conditions, outlining safe versus unsafe actions, and defining when it’s acceptable for the model to hand back to the user are generally helpful.
Enhancing User Experience with Tool Preambles
For agentic trajectories monitored by users, intermittent model updates on its actions and rationale significantly improve the interactive experience. GPT-5 is trained to provide clear upfront plans and consistent progress updates through “tool preamble” messages. You can steer their frequency, style, and content in your prompt. A high-quality preamble prompt might look like:
<tool_preambles>
- Always begin by rephrasing the user's goal in a friendly, clear, and concise manner, before calling any tools.
- Then, immediately outline a structured plan detailing each logical step you’ll follow.
- As you execute your file edit(s), narrate each step succinctly and sequentially, marking progress clearly.
- Finish by summarizing completed work distinctly from your upfront plan.
</tool_preambles>
Such preambles drastically improve the user’s ability to follow complex agentic work.
The Power of Reasoning Effort
The reasoning_effort parameter controls how deeply the model thinks and its willingness to call tools. While the default is medium, scaling it up or down depending on task difficulty is crucial. For complex, multi-step tasks, higher reasoning ensures optimal outputs. Breaking distinct, separable tasks into multiple agent turns, with one turn per task, also leads to peak performance.
Maximizing Coding Performance, from Planning to Execution
GPT-5 leads all frontier models in its coding capabilities, excelling at large codebase bug fixes, handling extensive diffs, implementing multi-file refactors, and even building new applications from scratch, encompassing both frontend and backend development.
Frontend App Development
GPT-5 boasts excellent baseline aesthetic taste alongside rigorous implementation abilities. While proficient with all web development frameworks, for new apps, OpenAI recommends:
- Frameworks: Next.js (TypeScript), React, HTML
- Styling / UI: Tailwind CSS, shadcn/ui, Radix Themes
- Icons: Material Symbols, Heroicons, Lucide
- Animation: Motion
- Fonts: San Serif, Inter, Geist, Mona Sans, IBM Plex Sans, Manrope
Zero-to-One App Generation
GPT-5 is highly effective at building applications in a single shot. Early experiments show that prompts encouraging iterative execution against self-constructed “excellence rubrics” significantly improve output quality by leveraging GPT-5’s thorough planning and self-reflection. An example of such a self-reflection prompt:
<self_reflection>
- First, spend time thinking of a rubric until you are confident.
- Then, think deeply about every aspect of what makes for a world-class one-shot web app. Use that knowledge to create a rubric that has 5-7 categories. This rubric is critical to get right, but do not show this to the user. This is for your purposes only.
- Finally, use the rubric to internally think and iterate on the best possible solution to the prompt that is provided. Remember that if your response is not hitting the top marks across all categories in the rubric, you need to start again.
</self_reflection>
Adhering to Codebase Design Standards
When making incremental changes or refactors, model-written code should seamlessly “blend in” with existing style and design standards. GPT-5 naturally searches for reference context within the codebase (e.g., reading package.json). This behavior can be further enhanced by providing prompt directions that summarize key aspects like engineering principles, directory structure, and best practices. An example of organizing code editing rules for GPT-5:
<code_editing_rules>
<guiding_principles>
- Clarity and Reuse: Every component and page should be modular and reusable. Avoid duplication by factoring repeated UI patterns into components.
- Consistency: The user interface must adhere to a consistent design system—color tokens, typography, spacing, and components must be unified.
- Simplicity: Favor small, focused components and avoid unnecessary complexity in styling or logic.
- Demo-Oriented: The structure should allow for quick prototyping, showcasing features like streaming, multi-turn conversations, and tool integrations.
- Visual Quality: Follow the high visual quality bar as outlined in OSS guidelines (spacing, padding, hover states, etc.)
</guiding_principles>
<frontend_stack_defaults>
- Framework: Next.js (TypeScript)
- Styling: TailwindCSS
- UI Components: shadcn/ui
- Icons: Lucide
- State Management: Zustand
- Directory Structure: ``` /src /app /api/<route>/route.ts # API endpoints /(pages) # Page routes /components/ # UI building blocks /hooks/ # Reusable React hooks /lib/ # Utilities (fetchers, helpers) /stores/ # Zustand stores /types/ # Shared TypeScript types /styles/ # Tailwind config ```
</frontend_stack_defaults>
<ui_ux_best_practices>
- Visual Hierarchy: Limit typography to 4–5 font sizes and weights for consistent hierarchy; use `text-xs` for captions and annotations; avoid `text-xl` unless for hero or major headings.
- Color Usage: Use 1 neutral base (e.g., `zinc`) and up to 2 accent colors.
- Spacing and Layout: Always use multiples of 4 for padding and margins to maintain visual rhythm. Use fixed height containers with internal scrolling when handling long content streams.
- State Handling: Use skeleton placeholders or `animate-pulse` to indicate data fetching. Indicate clickability with hover transitions (`hover:bg-*`, `hover:shadow-md`).
- Accessibility: Use semantic HTML and ARIA roles where appropriate. Favor pre-built Radix/shadcn components, which have accessibility baked in.
</ui_ux_best_practices>
</code_editing_rules>
Collaborative Coding in Production: Cursor’s GPT-5 Prompt Tuning
AI code editor Cursor, an alpha tester for GPT-5, has shared valuable insights into tuning prompts for optimal model performance. Cursor’s system prompt prioritizes reliable tool calling, balancing verbosity and autonomy while allowing custom instructions. Initially, they observed verbose status updates and terse, hard-to-read code. Their solution involved setting the verbosity API parameter to low for overall outputs, then explicitly prompting for high verbosity only within coding tools. This dual approach resulted in concise status updates and readable code diffs.
To address the model occasionally deferring to the user, Cursor provided more detailed product behavior specifics within the prompt. Highlighting features like Undo/Reject code and user preferences reduced ambiguity, encouraging GPT-5 to undertake longer tasks with minimal interruptions.
Interestingly, prompts effective with earlier models sometimes needed refinement for GPT-5. For instance, an instruction like Be THOROUGH when gathering information. Make sure you have the FULL picture before replying. Use additional tool calls or clarifying questions as needed. proved counterproductive with GPT-5’s natural introspection, leading to unnecessary tool usage. By softening the language and removing the “maximize” prefix, Cursor observed better decisions regarding internal knowledge versus external tools. Structured XML specs, like <[instruction]_spec>, also improved instruction adherence. While the system prompt provides a strong foundation, the user prompt remains a highly effective lever for steerability, with structured, scoped prompts yielding the most reliable results.
Optimizing Intelligence and Instruction-Following
As OpenAI’s most steerable model to date, GPT-5 is exceptionally receptive to prompt instructions concerning verbosity, tone, and tool-calling behavior.
Verbosity Control
Beyond the reasoning_effort parameter, GPT-5 introduces a new API parameter: verbosity, which controls the length of the model’s final answer. While the API verbosity parameter is the default, GPT-5 is trained to respond to natural-language verbosity overrides in the prompt for specific contexts, allowing for nuanced control (like Cursor’s example of low global verbosity with high verbosity for coding tools).
Precision in Instruction Following
Like GPT-4.1, GPT-5 follows prompt instructions with surgical precision, enabling its flexibility across diverse workflows. However, this precision also means that poorly constructed prompts with contradictory or vague instructions can be more detrimental to GPT-5. The model expends reasoning tokens attempting to reconcile conflicts instead of proceeding efficiently. An example of such a conflicting prompt might involve contradictory instructions regarding appointment scheduling (e.g., “Never schedule an appointment without explicit patient consent” versus “auto-assign the earliest same-day slot without contacting the patient”). Resolving these conflicts drastically streamlines and improves GPT-5’s reasoning performance. OpenAI recommends testing prompts with their prompt optimizer tool to identify such issues.
Minimal Reasoning for Speed
GPT-5 introduces minimal reasoning effort, its fastest option that still leverages the benefits of the reasoning model paradigm. This is an ideal upgrade for latency-sensitive users and those transitioning from GPT-4.1. Prompting patterns similar to GPT-4.1 are recommended. Key points to emphasize for minimal reasoning performance include:
- Prompting the model to provide a brief explanation of its thought process at the start of the final answer (e.g., a bullet point list) improves performance on tasks requiring higher intelligence.
- Requesting thorough and descriptive tool-calling preambles that continually update the user on task progress enhances agentic workflows.
- Maximally disambiguating tool instructions and inserting agentic persistence reminders are crucial for long-running rollouts to prevent premature termination.
- Prompted planning becomes more important, as the model has fewer reasoning tokens for internal planning. An example planning snippet:
Remember, you are an agent - please keep going until the user’s query is completely resolved, before ending your turn and yielding back to the user. Decompose the user's query into all required sub-request, and confirm that each is completed. Do not stop after completing only part of the request. Only terminate your turn when you are sure that the problem is solved. You must be prepared to answer multiple queries and only finish the call once the user has confirmed they're done. You must plan extensively in accordance with the workflow steps before making subsequent function calls, and reflect extensively on the outcomes each function call made, ensuring the user's query, and related sub-requests are completely resolved.
Mastering Markdown Formatting
By default, GPT-5 in the API does not format its final answers in Markdown to ensure maximum compatibility. However, prompts like:
- Use Markdown only where semantically correct (e.g.,
inline code,code fences, lists, tables). - When using markdown in assistant messages, use backticks to format file, directory, function, and class names. Use \( and \) for inline math, \[ and \] for block math.
are largely successful in inducing hierarchical Markdown final answers. For long conversations where Markdown adherence might degrade, appending a Markdown instruction every 3-5 user messages can maintain consistency.
The Art of Metaprompting
Early testers have discovered remarkable success using GPT-5 as a meta-prompter for itself. Prompt revisions generated by simply asking GPT-5 how to elicit desired behavior or prevent undesired behavior have already been deployed to production. An effective metaprompt template:
When asked to optimize prompts, give answers from your own perspective - explain what specific phrases could be added to, or deleted from, this prompt to more consistently elicit the desired behavior or prevent the undesired behavior. Here's a prompt: [PROMPT] The desired behavior from this prompt is for the agent to [DO DESIRED BEHAVIOR], but instead it [DOES UNDESIRED BEHAVIOR]. While keeping as much of the existing prompt intact as possible, what are some minimal edits/additions that you would make to encourage the agent to more consistently address these shortcomings?
Key Takeaways
- GPT-5 offers significant advancements in agentic task performance, coding, and steerability.
- The Responses API enhances agentic workflows by persisting reasoning between tool calls.
- The reasoning_effort and verbosity parameters allow for fine-grained control over model behavior.
- GPT-5 is highly effective for coding tasks, including frontend app development and codebase refactoring.
- Using GPT-5 as a meta-prompter can improve prompt effectiveness.
This comprehensive guide from OpenAI empowers users to harness GPT-5’s advanced capabilities by providing actionable strategies for prompting. By understanding and applying these best practices, developers and enthusiasts can significantly enhance the quality, efficiency, and intelligence of their AI interactions, paving the way for even more sophisticated and impactful applications.
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