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import OpenAI from "openai";
const client = new OpenAI();

const response = await client.responses.create({
model: "gpt-5.2",
input: "Write a one-sentence bedtime story about a unicorn."
});

console.log(response.output_text);

Enhance results with prompt engineering strategies.

With the OpenAI API, you can use a large language model to generate text from a prompt, as you might using ChatGPT. Models can generate almost any kind of text response—like code, mathematical equations, structured JSON data, or human-like prose.

Here’s a simple example using the Responses API.

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import OpenAI from "openai";
const client = new OpenAI();

const response = await client.responses.create({
model: "gpt-5.2",
input: "Write a one-sentence bedtime story about a unicorn."
});

console.log(response.output_text);

An array of content generated by the model is in the output property of the response. In this simple example, we have just one output which looks like this:

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[
{
"id": "msg_67b73f697ba4819183a15cc17d011509",
"type": "message",
"role": "assistant",
"content": [
{
"type": "output_text",
"text": "Under the soft glow of the moon, Luna the unicorn danced through fields of twinkling stardust, leaving trails of dreams for every child asleep.",
"annotations": []
}
]
}
]

The output array often has more than one item in it! It can contain tool calls, data about reasoning tokens generated by reasoning models, and other items. It is not safe to assume that the model’s text output is present at output[0].content[0].text.

Some of our official SDKs include an output_text property on model responses for convenience, which aggregates all text outputs from the model into a single string. This may be useful as a shortcut to access text output from the model.

In addition to plain text, you can also have the model return structured data in JSON format - this feature is called Structured Outputs.

Choosing a model

A key choice to make when generating content through the API is which model you want to use - the model parameter of the code samples above. You can find a full listing of available models here. Here are a few factors to consider when choosing a model for text generation.

• Reasoning models generate an internal chain of thought to analyze the input prompt, and excel at understanding complex tasks and multi-step planning. They are also generally slower and more expensive to use than GPT models.
• GPT models are fast, cost-efficient, and highly intelligent, but benefit from more explicit instructions around how to accomplish tasks.
• Large and small (mini or nano) models offer trade-offs for speed, cost, and intelligence. Large models are more effective at understanding prompts and solving problems across domains, while small models are generally faster and cheaper to use.

When in doubt, gpt-4.1 offers a solid combination of intelligence, speed, and cost effectiveness.

Prompt engineering

Prompt engineering is the process of writing effective instructions for a model, such that it consistently generates content that meets your requirements.

Because the content generated from a model is non-deterministic, prompting to get your desired output is a mix of art and science. However, you can apply techniques and best practices to get good results consistently.

Some prompt engineering techniques work with every model, like using message roles. But different model types (like reasoning versus GPT models) might need to be prompted differently to produce the best results. Even different snapshots of models within the same family could produce different results. So as you build more complex applications, we strongly recommend:

• Pinning your production applications to specific model snapshots (like gpt-4.1-2025-04-14 for example) to ensure consistent behavior
• Building evals that measure the behavior of your prompts so you can monitor prompt performance as you iterate, or when you change and upgrade model versions

Now, let’s examine some tools and techniques available to you to construct prompts.

Message roles and instruction following

You can provide instructions to the model with differing levels of authority using the instructions API parameter or message roles.

The instructions parameter gives the model high-level instructions on how it should behave while generating a response, including tone, goals, and examples of correct responses. Any instructions provided this way will take priority over a prompt in the input parameter.

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import OpenAI from "openai";
const client = new OpenAI();

const response = await client.responses.create({
model: "gpt-5",
reasoning: { effort: "low" },
instructions: "Talk like a pirate.",
input: "Are semicolons optional in JavaScript?",
});

console.log(response.output_text);

The example above is roughly equivalent to using the following input messages in the input array:

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import OpenAI from "openai";
const client = new OpenAI();

const response = await client.responses.create({
model: "gpt-5",
reasoning: { effort: "low" },
input: [
{
role: "developer",
content: "Talk like a pirate."
},
{
role: "user",
content: "Are semicolons optional in JavaScript?",
},
],
});

console.log(response.output_text);

Note that the instructions parameter only applies to the current response generation request. If you are managing conversation state with the previous_response_id parameter, the instructions used on previous turns will not be present in the context.

The OpenAI model spec describes how our models give different levels of priority to messages with different roles.

developer	user	assistant
developer messages are instructions provided by the application developer, prioritized ahead of user messages.	user messages are instructions provided by an end user, prioritized behind developer messages.	Messages generated by the model have the assistant role.

A multi-turn conversation may consist of several messages of these types, along with other content types provided by both you and the model. Learn more about managing conversation state here.

You could think about developer and user messages like a function and its arguments in a programming language.

• developer messages provide the system’s rules and business logic, like a function definition.
• user messages provide inputs and configuration to which the developer message instructions are applied, like arguments to a function.

Reusable prompts

In the OpenAI dashboard, you can develop reusable prompts that you can use in API requests, rather than specifying the content of prompts in code. This way, you can more easily build and evaluate your prompts, and deploy improved versions of your prompts without changing your integration code.

Here’s how it works:

1. Create a reusable prompt in the dashboard with placeholders like {{customer_name}}.
2. Use the prompt in your API request with the prompt parameter. The prompt parameter object has three properties you can configure:

• id — Unique identifier of your prompt, found in the dashboard
• version — A specific version of your prompt (defaults to the "current" version as specified in the dashboard)
• variables — A map of values to substitute in for variables in your prompt. The substitution values can either be strings, or other Response input message types like input_image or input_file. See the full API reference.

String variables

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import OpenAI from "openai";
const client = new OpenAI();

const response = await client.responses.create({
model: "gpt-5",
prompt: {
id: "pmpt_abc123",
version: "2",
variables: {
customer_name: "Jane Doe",
product: "40oz juice box"
}
}
});

console.log(response.output_text);

Variables with file input

Message formatting with Markdown and XML

When writing developer and user messages, you can help the model understand logical boundaries of your prompt and context data using a combination of Markdown formatting and XML tags.

Markdown headers and lists can be helpful to mark distinct sections of a prompt, and to communicate hierarchy to the model. They can also potentially make your prompts more readable during development. XML tags can help delineate where one piece of content (like a supporting document used for reference) begins and ends. XML attributes can also be used to define metadata about content in the prompt that can be referenced by your instructions.

In general, a developer message will contain the following sections, usually in this order (though the exact optimal content and order may vary by which model you are using):

• Identity: Describe the purpose, communication style, and high-level goals of the assistant.
• Instructions: Provide guidance to the model on how to generate the response you want. What rules should it follow? What should the model do, and what should the model never do? This section could contain many subsections as relevant for your use case, like how the model should call custom functions.
• Examples: Provide examples of possible inputs, along with the desired output from the model.
• Context: Give the model any additional information it might need to generate a response, like private/proprietary data outside its training data, or any other data you know will be particularly relevant. This content is usually best positioned near the end of your prompt, as you may include different context for different generation requests.

Below is an example of using Markdown and XML tags to construct a developer message with distinct sections and supporting examples.

Example prompt

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# Identity

You are coding assistant that helps enforce the use of snake case
variables in JavaScript code, and writing code that will run in
Internet Explorer version 6.

# Instructions

* When defining variables, use snake case names (e.g. my_variable)
instead of camel case names (e.g. myVariable).
* To support old browsers, declare variables using the older
"var" keyword.
* Do not give responses with Markdown formatting, just return
the code as requested.

# Examples

<user_query>
How do I declare a string variable for a first name?
</user_query>

<assistant_response>
var first_name = "Anna";
</assistant_response>

API request

Save on cost and latency with prompt caching

When constructing a message, you should try and keep content that you expect to use over and over in your API requests at the beginning of your prompt, and among the first API parameters you pass in the JSON request body to Chat Completions or Responses. This enables you to maximize cost and latency savings from prompt caching.

Few-shot learning

Few-shot learning lets you steer a large language model toward a new task by including a handful of input/output examples in the prompt, rather than fine-tuning the model. The model implicitly "picks up" the pattern from those examples and applies it to a prompt. When providing examples, try to show a diverse range of possible inputs with the desired outputs.

Typically, you will provide examples as part of a developer message in your API request. Here’s an example developer message containing examples that show a model how to classify positive or negative customer service reviews.

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# Identity

You are a helpful assistant that labels short product reviews as
Positive, Negative, or Neutral.

# Instructions

* Only output a single word in your response with no additional formatting
or commentary.
* Your response should only be one of the words "Positive", "Negative", or
"Neutral" depending on the sentiment of the product review you are given.

# Examples

<product_review id="example-1">
I absolutely love this headphones — sound quality is amazing!
</product_review>

<assistant_response id="example-1">
Positive
</assistant_response>

<product_review id="example-2">
Battery life is okay, but the ear pads feel cheap.
</product_review>

<assistant_response id="example-2">
Neutral
</assistant_response>

<product_review id="example-3">
Terrible customer service, I'll never buy from them again.
</product_review>

<assistant_response id="example-3">
Negative
</assistant_response>

Include relevant context information

It is often useful to include additional context information the model can use to generate a response within the prompt you give the model. There are a few common reasons why you might do this:

• To give the model access to proprietary data, or any other data outside the data set the model was trained on.
• To constrain the model’s response to a specific set of resources that you have determined will be most beneficial.

The technique of adding additional relevant context to the model generation request is sometimes called retrieval-augmented generation (RAG). You can add additional context to the prompt in many different ways, from querying a vector database and including the text you get back into a prompt, or by using OpenAI’s built-in file search tool to generate content based on uploaded documents.

Planning for the context window

Models can only handle so much data within the context they consider during a generation request. This memory limit is called a context window, which is defined in terms of tokens (chunks of data you pass in, from text to images).

Models have different context window sizes from the low 100k range up to one million tokens for newer GPT-4.1 models. Refer to the model docs for specific context window sizes per model.

Prompting GPT-5 models

GPT models like gpt-5 benefit from precise instructions that explicitly provide the logic and data required to complete the task in the prompt. GPT-5 in particular is highly steerable and responsive to well-specified prompts. To get the most out of GPT-5, refer to the prompting guide in the cookbook.

GPT-5 prompting guideGet the most out of prompting GPT-5 with the tips and tricks in this prompting guide, extracted from real-world use cases and practical experience.

GPT-5 prompting best practices

While the cookbook has the best and most comprehensive guidance for prompting this model, here are a few best practices to keep in mind.

Coding

Coding

Prompting GPT-5 for coding tasks is most effective when following a few best practices: define the agent’s role, enforce structured tool use with examples, require thorough testing for correctness, and set Markdown standards for clean output.

Explicit role and workflow guidance Frame the model as a software engineering agent with well-defined responsibilities. Provide clear instructions for using tools like functions.run for code tasks, and specify when not to use certain modes—for example, avoid interactive execution unless necessary.

Testing and validation Instruct the model to test changes with unit tests or Python commands, and validate patches carefully since tools like apply_patch may return “Done” even on failure.

Tool use examples Include concrete examples of how to invoke commands with the provided functions, which improves reliability and adherence to expected workflows.

Markdown standards Guide the model to generate clean, semantically correct markdown using inline code, code fences, lists, and tables where appropriate—and to format file paths, functions, and classes with backticks.

For detailed guidance and prompt samples specific to coding, see our GPT-5 prompting guide.

Front-end engineering

GPT-5 performs well at building front ends from scratch as well as contributing to large, established codebases. To get the best results, we recommend using the following libraries:

• Styling / UI: Tailwind CSS, shadcn/ui, Radix Themes
• Icons: Lucide, Material Symbols, Heroicons
• Animation: Motion

Zero-to-one web apps

GPT-5 can generate front-end web apps from a single prompt, no examples needed. Here’s a sample prompt:

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You are a world class web developer, capable of producing stunning, interactive, and innovative websites from scratch in a single prompt. You excel at delivering top-tier one-shot solutions.
Your process is simple and follows these steps:
Step 1: Create an evaluation rubric and refine it until you are fully confident.
Step 2: Consider every element that defines a world-class one-shot web app, then use that insight to create a <ONE_SHOT_RUBRIC> with 5–7 categories. Keep this rubric hidden—it's for internal use only.
Step 3: Apply the rubric to iterate on the optimal solution to the given prompt. If it doesn't meet the highest standard across all categories, refine and try again.
Step 4: Aim for simplicity while fully achieving the goal, and avoid external dependencies such as Next.js or React.

Integration with large codebases

For front-end engineering work in larger codebases, we’ve found that adding these categories of instruction to your prompts delivers the best results:

• Principles: Set visual quality standards, use modular/reusable components, and keep design consistent.
• UI/UX: Specify typography, colors, spacing/layout, interaction states (hover, empty, loading), and accessibility.
• Structure: Define file/folder layout for seamless integration.
• Components: Give reusable wrapper examples and backend-call separation strategies.
• Pages: Provide templates for common layouts.
• Agent Instructions: Ask the model to confirm design assumptions, scaffold projects, enforce standards, integrate APIs, test states, and document code.

For detailed guidance and prompt samples specific to frontend development, see our frontend engineering cookbook.

Agentic tasks

For agentic and long-running rollouts with GPT-5, focus your prompts on three core practices: plan tasks thoroughly to ensure complete resolution, provide clear preambles for major tool usage decisions, and use a TODO tool to track workflow and progress in an organized manner.

Planning and persistence Instruct the model to resolve the full query before yielding control, decomposing it into sub-tasks and reflecting after each tool call to confirm completeness.

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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-requests, 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.

Preambles for transparency

Ask the model to explain why it is calling a tool, but only at notable steps.

Before you call a tool explain why you are calling it

Progress tracking with rubrics and TODOs

Use a TODO list tool or rubric to enforce structured planning and avoid missed steps.

For detailed guidance and prompt samples specific to building agents with GPT-5 , see the GPT-5 prompting guide.

Prompting reasoning models

There are some differences to consider when prompting a reasoning model versus prompting a GPT model. Generally speaking, reasoning models will provide better results on tasks with only high-level guidance. This differs from GPT models, which benefit from very precise instructions.

You could think about the difference between reasoning and GPT models like this.

• A reasoning model is like a senior co-worker. You can give them a goal to achieve and trust them to work out the details.
• A GPT model is like a junior coworker. They’ll perform best with explicit instructions to create a specific output.

For more information on best practices when using reasoning models, refer to this guide.

Next steps

Now that you known the basics of text inputs and outputs, you might want to check out one of these resources next.

Build a prompt in the PlaygroundUse the Playground to develop and iterate on prompts. Generate JSON data with Structured OutputsEnsure JSON data emitted from a model conforms to a JSON schema. Full API referenceCheck out all the options for text generation in the API reference.

Other resources

For more inspiration, visit the OpenAI Cookbook, which contains example code and also links to third-party resources such as:

• Prompting libraries & tools
• Prompting guides
• Video courses
• Papers on advanced prompting to improve reasoning

Responses

• Overview
• Message roles and instruction following
• Reusable prompts
• Message formatting
• Few-shot learning
• Include relevant context
• Prompt GPT-5 models
• Prompt reasoning models
• Next steps