The GPT‑5 model that OpenAI released on August 7, 2025, marks a major leap in AI reasoning capabilities, and its accompanying "GPT‑5 Prompting Guide" (hereafter the "Guide") provides users and developers with comprehensive prompt engineering best practices. Based on months of internal testing by OpenAI teams, the Guide covers techniques from basic prompt design to advanced agentic tasks across roughly 50 pages, aiming to help users maximize GPT‑5's potential. Compared with prior models such as GPT‑4o, GPT‑5 introduces "thinking modes" (GPT‑5 thinking) and hybrid reasoning paths, emphasizing optimization for agentic tasks, instruction adherence, and new API features. The Guide's core philosophy is "less is more": by using structured, precise prompts, reduce hallucinations and improve response accuracy and efficiency. Below is a summary of the Guide's main points, organized into five modules—basic techniques, reasoning optimization, tool invocation, agent tasks, and best practices—with explanations and concrete examples (example word counts are not included in the total).
1. Basic Prompt Design: Clarity and Structure
The Guide stresses that GPT‑5's performance is highly dependent on prompt clarity and structure. Vague prompts lead to biased responses, while structured prompts guide the model to reason step by step. Key techniques include using stepwise instructions, role‑playing, and context grounding. For example, the Guide recommends dividing prompts into three parts: "task description," "input data," and "output format" to reduce ambiguity. At the same time, avoid overly long prompts (ideal length <2000 tokens) and use delimiters such as ### or <step> to organize content. This can significantly lower the model's error rate, especially on multilingual or domain‑specific tasks.
The Guide also notes that GPT‑5 is sensitive to keywords: the model will automatically emphasize keywords in a prompt, so using precise terminology improves response relevance. In addition, incorporating few‑shot examples can further steer model behavior and avoid generalization issues seen in zero‑shot settings. Using these basic methods, users can turn simple queries into efficient interactions.
Example 1: Basic structured prompt (for summarizing an article)
User: Summarize the following article in 3 key points.
Article: [paste article text]
Output format: ‑ Point 1: [summary] ‑ Point 2: [summary] ‑ Point 3: [summary]
Example 2: Role‑playing prompt (for customer service simulation)
User: You are a helpful customer service agent. Respond to this query politely and solve the issue step by step. Query: My order is delayed.
2. Reasoning Optimization: Using Thinking Modes and Effort Levels
A core innovation in GPT‑5 is "thinking modes." The Guide details how to control the model's reasoning depth by adjusting the "reasoning effort" parameter (low/medium/high). High effort levels suit complex tasks and reduce errors but increase latency. The Guide recommends explicitly requesting Chain‑of‑Thought (CoT) reasoning in prompts—asking the model to break problems down step by step, such as "first analyze the problem, then list the steps, then provide the answer." This can improve accuracy on mathematical or logical tasks by over 30%.
The Guide also introduces "persistence rules": include instructions like "if unsure, continue thinking until resolved" or "check output consistency" to prevent premature stopping. For agentic tasks, the Guide suggests combining an "escape hatch" mechanism: if the model gets stuck, allow it to request more information. This enhances GPT‑5's stability in long‑chain reasoning, particularly for scientific problem solving.
Example 3: CoT reasoning prompt (math problem)
User: Solve this equation: x^2 + 5x + 6 = 0. Use high reasoning effort. Step 1: Factor the equation. Step 2: Find roots. Step 3: Verify.
Example 4: Persistence rules prompt (research task)
User: Research the impact of AI on jobs. If data is insufficient, search for more sources and continue until you have 5 key insights.
3. Tool Invocation and Integration: Enhancing Agent Capabilities
The Guide focuses on GPT‑5's tool invocation features, which support Python code execution, search, and external API integration. Developers should specify tool usage rules in prompts, such as "use the Python tool for calculations when needed." New API features include strict function calling to ensure outputs conform to schema definitions and avoid JSON parsing errors. The Guide recommends using a "tool choice" parameter to lock tool selection for specific tasks.
For multi‑tool chains, the Guide advocates an "agentic workflow": the model first plans a tool sequence, then executes it. This is particularly effective for programming and data analysis, raising task completion rates from 70% to 95%. The Guide also warns against excessive tool invocation to avoid higher costs, suggesting prompts include "only use tools when necessary."
Example 5: Tool invocation prompt (weather query)
User: Get the weather in Beijing. Use the weather API tool. Format: Temperature: [value] Condition: [description]
Example 6: Multi‑tool chain prompt (stock analysis)
User: Analyze AAPL stock. Step 1: Use search tool for latest price. Step 2: Use Python tool to calculate moving average. Step 3: Summarize trends.
4. Agent Tasks and Instruction Adherence: Building Reliable AI Agents
Agentic behavior is a central focus of the Guide, which emphasizes ensuring strict instruction adherence. Techniques include using system prompts to define rules like "always output JSON format" or "if a rule is violated, retry." The Guide introduces prompt optimization using GPT‑5 itself—for example, generating improved prompts via a "Prompt Optimizer" API.
For agent tasks, the Guide recommends building loop‑based agents: the model cycles through observe‑think‑act loops until the task is complete. This approach suits chatbots and automation scripts. The Guide also suggests incorporating few‑shot chains—providing multiple example chains to help the model learn complex processes.
Example 7: Instruction adherence prompt (JSON output)
User: List 3 fruits. Output in JSON: {"fruits": ["apple", "banana", "cherry"]}. If not JSON, retry.
Example 8: Agent loop prompt (task management)
User: Plan my day. Observe: Schedule meetings. Think: Prioritize tasks. Act: Output calendar. Loop until complete.
5. Best Practices and Caveats: Avoiding Common Pitfalls
The Guide summarizes common mistakes such as overly complex prompts or ignoring cultural biases, and offers remedies: test multiple variants and use A/B testing for optimization. It emphasizes safety by adding "reject harmful requests" rules. The Guide also discusses GPT‑5 "frontend prompting": visualizing prompt optimization in the dashboard.
Overall, the Guide reflects OpenAI's pursuit of AI controllability, helping users move from passive querying to proactive agenting. As GPT‑5 evolves, these techniques are expected to develop further and drive AI application innovation.
Source: GPT‑5 prompting guide