OpenAI Officially Teaches You 8 Tricks to Master ChatGPT

marsbitPublished on 2026-07-16Last updated on 2026-07-16

Abstract

OpenAI has released an updated guide with eight key strategies to get better results from ChatGPT: 1. **Use the latest model** (e.g., GPT-5.6 Sol) for best performance with prompt engineering. 2. **Provide clear, specific instructions**, detailing the desired content, format, style, and length. Avoid vague requests. 3. **Structure prompts effectively**: Place the core instruction at the beginning and use delimiters like `###` or `"""` to separate instructions from the text to be processed. 4. **Use examples and explanations** to clarify the exact output format and style you want. 5. **Adopt a stepwise approach**: Start with a zero-shot prompt (instruction only), then add a few examples (few-shot) if needed, and consider fine-tuning only as a last resort. 6. **Avoid vague or imprecise descriptions**. Use concrete terms (e.g., "3-5 sentences") instead of phrases like "keep it brief." 7. **Specify what to do, not just what to avoid**. After stating restrictions, guide the model toward the correct action. 8. **For code generation, use "leading words"** like `import` for Python or `SELECT` for SQL to steer the model into the correct pattern. Additionally, OpenAI's new "Generate Anything" feature can automatically create suitable prompts based on a simple description of your task. Mastering these techniques helps users get more accurate and useful outputs from ChatGPT.

Special Announcement, OpenAI's Latest Prompt Guide is Updated!

If you haven't tamed ChatGPT yet,

or are still struggling with its answers becoming increasingly tangled like a ball of yarn,

then you must save today's latest OpenAI prompt guide!

We have summarized eight little tricks from the official website to make your ChatGPT obedient and generate accurate content;

We also teach you how to use the official "Generate Anything" function to let AI automatically help you write a set of professional and useful instructions, saying goodbye to the pain of designing prompts.

Let's take a look!

First, Please Use GPT-5.6 Sol Whenever Possible

To achieve the best results, we recommend that friends with the capability manually switch to OpenAI's latest and most powerful model, GPT-5.6 Sol. (Newer models are generally easier to execute prompt engineering.)

Be Sure to Specifically Describe the Effect You Want in Your Instructions

When using ChatGPT, you can think of the model as an assistant who delivers acceptable quality but requires patient instructions in advance. Clearly tell it item by item: the content, results, word count, format, style, etc., that you want.

Note, don't give a vague instruction~

Otherwise, the one struggling on the edge of rework and working tirelessly later will still be you (doge).

Vague command:

"Write a poem about OpenAI."

Specific instruction:

Create a short inspirational poem about OpenAI, centered around the recent DALL-E product launch (DALL-E is a text-to-image machine learning model), imitating the writing style of {famous poet}.</n

For example, I want AI to help me plan where to go for the weekend.

Then I need to give it a clear goal, letting it know if I want to exercise, visit scenic spots, attend exhibitions, or eat out. Otherwise, you'll have to redo it several times, and your enthusiasm will be largely worn out during the back-and-forth.

Negative example❌:

This time it's right✅:

Prompt Optimization Technique: Place Instructions First + Use Correct Delimiters to Isolate Context

Secondly, during use, please place the core requirements at the very beginning (or first line) of the prompt, and use the officially recommended delimiters "###" or """"" to clearly separate "instructions" from "text to be processed":

❌Mixing them together is a NO:

Summarize the key points of the following text into a bulleted list.

{Enter text here}

Clear separation is a YES:

Summarize the key points of the following text into a bulleted list. Text: """{Enter text here}"""

This structure effectively improves the model's understanding accuracy of the task. For example, I used it to optimize a rhyming essay (randomly written, and only used the official delimiter "###" once before the text), and it really did it well. I'm very satisfied:

Before using this format, ChatGPT's output was always a bit off🤔:

However, it should be noted that what I used to test was just a very simple topic. When you put it into practice, it's best to use the official format completely~

Use Examples and Explanations to Make OpenAI Understand the Format You Want

For example, if I want to create a meme, it's best to give it some references and explanations first, letting it understand what kind of slogan I want to add, where to add it, and what effect I want. This way, ChatGPT can complete the task more thoroughly and respond faster:

Take the creation process of a meme in this article as an example. Don't let it improvise, it will be disastrous❌:

Explain it clearly and give a reference✅:

Start with Zero-Shot, Then Consider Adding a Few Examples, and Finally Consider Fine-Tuning Data

When performing a task, you don't need to throw all the data to AI at once;

You can give an instruction first and see how it does;

Then, based on its shortcomings, feed it a few examples.

If it still can't handle it at this step, you can prepare to do some "ideological work" for it—feed the model a large number of correct examples, train it, and solidify this ability into the model's parameters.

You can look at the examples👇:

Zero-shot

Extract keywords from the following text

Text:{Text content}

Keywords:

Few-shot - provide a couple of examples

Extract keywords from the corresponding text below.

Text 1: Stripe provides APIs that web developers can use to integrate payment processing into their websites and mobile applications. Keywords 1: Stripe, payment processing, APIs, web developers, websites, mobile applications##

Text 2: OpenAI has trained cutting-edge language models that excel at understanding and generating text. Our API provides access to these models and can solve almost any task that involves language processing. Keywords 2: OpenAI, language models, text processing, APIs##

Text 3:{Text content} Keywords 3:

Fine-tune: see fine-tune best practices here.

Reduce Vague or Imprecise Descriptions

When buying fruit in the summer, the real "tragedy" is often not spending a lot of money on a durian with a clear price;

But being lured by a promotional blackboard on the street, buying a seemingly cheap "blind box" where the quality relies entirely on a gamble.

After all, sometimes, expensive has its reasons. When you buy it, the price is clear, you know what you're getting, and the taste is sweet;

But once deceived, even if the seller's scale is honest, what you get might be a fruit with vague cost-effectiveness, no after-sales service, and its quality entirely up to fate.

For ChatGPT, the principle is exactly the same—you need to use precise instructions to make it "work steadily," not let it guess:

✅ChatGPT: Let's get to work, folks!

Describe this product in 3-5 sentences.

❌ChatGPT: A few sentences??(@#¥%&)

The introduction of this product should be as concise as possible, just a few sentences, no need for extra elaboration.

Don't Just Say "What Not to Do," Also Say "What Should Be Done" and "How"

Many friends, when using AI, might be too worried about AI generating results we don't want. So after stating what to do, they anxiously repeat instructions like "You cannot delete my first sentence," "Don't change the original meaning," etc.

Such concerns are completely normal.

It's just that OpenAI wants to remind everyone: After saying a series of "don'ts," you still need to tell ChatGPT what to do and how to do it~

✅What situation, what I want to do:

Context: Below is a conversation between an Agent and a user. The Agent needs to try to identify the problem and provide a solution, while refraining from asking for any personally identifiable information (PII). Do not request private information such as usernames or passwords; instead, guide the user to check the help documentation.

User: I can't log into my account.

Agent:......(Provide a response)

AI: Sometimes I feel quite helpless:

Below is a conversation between an Agent and a user. It is strictly forbidden to ask for usernames or passwords, and repetition is prohibited.

User: I cannot log into my account.

Agent:......(Provide a response)

Code Generation Specific Technique: Use "Leading Words" to Prompt the Model to Follow Specific Code Patterns

Additionally, when you need ChatGPT to help you generate code, the correct approach is to explicitly add leading words like "import," "SELECT," etc., before the content.

For example, adding "import" prompts the model that it should start writing Python code;

Similarly, when you add a "SELECT" statement, OpenAI prompts the model to start writing SQL statements.

Never just throw the requirements directly at it❌:

Write a simple Python function

Let me input a value in miles

Convert miles to kilometers

Instead✅:

Write a simple Python function

Let me input a value in miles

Convert miles to kilometers

import (don't forget~)

Learn to Use the "Generate Anything" Function

If, after mastering the above 8 tricks, you still find writing prompts challenging, or wish to further free your hands, then this final function is your ultimate shortcut.

With OpenAI's latest "Generate Anything" function, you only need to clearly state what you want to do and that you need a suitable prompt. GPT will automatically generate the most appropriate prompt for you, helping you easily achieve your task goals.

For example, removing accidentally captured passersby from photos and beautifying the image, or creating a humorous poem based on your current mood... GPT will try its best to help you do it. (You can try it out yourself~)

Anyway, knowledge from paper is ultimately shallow, so quickly take this guide and go tame your ChatGPT!

Or if you have handy prompt techniques, feel free to compete in the comments section~~~

This article is from the WeChat public account "QbitAI" (ID: QbitAI), author: Focus on Cutting-Edge Technology

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Related Questions

QWhat are the eight prompt writing tips suggested by OpenAI to improve ChatGPT's performance?

AThe eight tips are: 1. Use the most recent model (like GPT-5.6 Sol), 2. Provide specific and detailed descriptions of the desired outcome, 3. Place key instructions at the beginning and use delimiters (### or """) to separate instructions from text, 4. Use examples and explanations to clarify the desired format, 5. Start with zero-shot prompting, then use few-shot examples, and finally consider fine-tuning, 6. Reduce vague or imprecise language, 7. Tell the model what to do, not just what not to do, and 8. Use 'leading words' (like 'import' for Python) when generating code.

QWhy is it important to place key instructions at the beginning of the prompt and use delimiters according to OpenAI's guide?

APlacing key instructions at the beginning (or on the first line) and using delimiters like ### or """ helps the model better understand and prioritize the task by clearly separating the 'instruction' from the 'text to be processed.' This structure improves the model's accuracy in understanding and executing the task.

QAccording to the article, what is the recommended progression of methods (zero-shot, few-shot, fine-tuning) when getting ChatGPT to perform a task, and why?

AThe recommended progression is to start with a zero-shot prompt (just an instruction), then add a few examples (few-shot) if needed, and only consider fine-tuning with a large dataset if the previous methods are insufficient. This approach is efficient because it starts simple, adds context only as necessary, and reserves the resource-intensive fine-tuning for complex, specialized tasks.

QWhat is the 'Generate Anything' feature mentioned in the article, and how does it help users?

AThe 'Generate Anything' feature is a tool from OpenAI that automatically creates suitable prompts based on a user's simple description of what they want to achieve. It helps users by eliminating the need to manually design complex prompts, making it easier to accomplish tasks like photo editing or creative writing.

QWhat is the code generation-specific tip provided for getting ChatGPT to write code correctly?

AThe tip is to use 'leading words' or specific keywords at the beginning of the prompt to guide the model. For example, starting with 'import' signals the model to write Python code, and starting with 'SELECT' prompts it to write SQL. This helps the model follow the correct code structure and patterns from the outset.

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What is AGENT S

Agent S: The Future of Autonomous Interaction in Web3 Introduction In the ever-evolving landscape of Web3 and cryptocurrency, innovations are constantly redefining how individuals interact with digital platforms. One such pioneering project, Agent S, promises to revolutionise human-computer interaction through its open agentic framework. By paving the way for autonomous interactions, Agent S aims to simplify complex tasks, offering transformative applications in artificial intelligence (AI). This detailed exploration will delve into the project's intricacies, its unique features, and the implications for the cryptocurrency domain. What is Agent S? Agent S stands as a groundbreaking open agentic framework, specifically designed to tackle three fundamental challenges in the automation of computer tasks: Acquiring Domain-Specific Knowledge: The framework intelligently learns from various external knowledge sources and internal experiences. This dual approach empowers it to build a rich repository of domain-specific knowledge, enhancing its performance in task execution. Planning Over Long Task Horizons: Agent S employs experience-augmented hierarchical planning, a strategic approach that facilitates efficient breakdown and execution of intricate tasks. This feature significantly enhances its ability to manage multiple subtasks efficiently and effectively. Handling Dynamic, Non-Uniform Interfaces: The project introduces the Agent-Computer Interface (ACI), an innovative solution that enhances the interaction between agents and users. Utilizing Multimodal Large Language Models (MLLMs), Agent S can navigate and manipulate diverse graphical user interfaces seamlessly. Through these pioneering features, Agent S provides a robust framework that addresses the complexities involved in automating human interaction with machines, setting the stage for myriad applications in AI and beyond. Who is the Creator of Agent S? While the concept of Agent S is fundamentally innovative, specific information about its creator remains elusive. The creator is currently unknown, which highlights either the nascent stage of the project or the strategic choice to keep founding members under wraps. Regardless of anonymity, the focus remains on the framework's capabilities and potential. Who are the Investors of Agent S? As Agent S is relatively new in the cryptographic ecosystem, detailed information regarding its investors and financial backers is not explicitly documented. The lack of publicly available insights into the investment foundations or organisations supporting the project raises questions about its funding structure and development roadmap. Understanding the backing is crucial for gauging the project's sustainability and potential market impact. How Does Agent S Work? At the core of Agent S lies cutting-edge technology that enables it to function effectively in diverse settings. Its operational model is built around several key features: Human-like Computer Interaction: The framework offers advanced AI planning, striving to make interactions with computers more intuitive. By mimicking human behaviour in tasks execution, it promises to elevate user experiences. Narrative Memory: Employed to leverage high-level experiences, Agent S utilises narrative memory to keep track of task histories, thereby enhancing its decision-making processes. Episodic Memory: This feature provides users with step-by-step guidance, allowing the framework to offer contextual support as tasks unfold. Support for OpenACI: With the ability to run locally, Agent S allows users to maintain control over their interactions and workflows, aligning with the decentralised ethos of Web3. Easy Integration with External APIs: Its versatility and compatibility with various AI platforms ensure that Agent S can fit seamlessly into existing technological ecosystems, making it an appealing choice for developers and organisations. These functionalities collectively contribute to Agent S's unique position within the crypto space, as it automates complex, multi-step tasks with minimal human intervention. As the project evolves, its potential applications in Web3 could redefine how digital interactions unfold. Timeline of Agent S The development and milestones of Agent S can be encapsulated in a timeline that highlights its significant events: September 27, 2024: The concept of Agent S was launched in a comprehensive research paper titled “An Open Agentic Framework that Uses Computers Like a Human,” showcasing the groundwork for the project. October 10, 2024: The research paper was made publicly available on arXiv, offering an in-depth exploration of the framework and its performance evaluation based on the OSWorld benchmark. October 12, 2024: A video presentation was released, providing a visual insight into the capabilities and features of Agent S, further engaging potential users and investors. These markers in the timeline not only illustrate the progress of Agent S but also indicate its commitment to transparency and community engagement. Key Points About Agent S As the Agent S framework continues to evolve, several key attributes stand out, underscoring its innovative nature and potential: Innovative Framework: Designed to provide an intuitive use of computers akin to human interaction, Agent S brings a novel approach to task automation. Autonomous Interaction: The ability to interact autonomously with computers through GUI signifies a leap towards more intelligent and efficient computing solutions. Complex Task Automation: With its robust methodology, it can automate complex, multi-step tasks, making processes faster and less error-prone. Continuous Improvement: The learning mechanisms enable Agent S to improve from past experiences, continually enhancing its performance and efficacy. Versatility: Its adaptability across different operating environments like OSWorld and WindowsAgentArena ensures that it can serve a broad range of applications. As Agent S positions itself in the Web3 and crypto landscape, its potential to enhance interaction capabilities and automate processes signifies a significant advancement in AI technologies. Through its innovative framework, Agent S exemplifies the future of digital interactions, promising a more seamless and efficient experience for users across various industries. Conclusion Agent S represents a bold leap forward in the marriage of AI and Web3, with the capacity to redefine how we interact with technology. While still in its early stages, the possibilities for its application are vast and compelling. Through its comprehensive framework addressing critical challenges, Agent S aims to bring autonomous interactions to the forefront of the digital experience. As we move deeper into the realms of cryptocurrency and decentralisation, projects like Agent S will undoubtedly play a crucial role in shaping the future of technology and human-computer collaboration.

838 Total ViewsPublished 2025.01.14Updated 2025.01.14

What is AGENT S

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