Example prompt
A developer message for code generation
```text
# 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
How do I declare a string variable for a first name?
var first_name = "Anna";
```
API request
Send a prompt to generate code through the API
```javascript
import fs from "fs/promises";
import OpenAI from "openai";
const client = new OpenAI();
const instructions = await fs.readFile("prompt.txt", "utf-8");
const response = await client.responses.create({
model: "gpt-5.5",
instructions,
input: "How would I declare a variable for a last name?",
});
console.log(response.output_text);
```
```python
from openai import OpenAI
client = OpenAI()
with open("prompt.txt", "r", encoding="utf-8") as f:
instructions = f.read()
response = client.responses.create(
model="gpt-5.5",
instructions=instructions,
input="How would I declare a variable for a last name?",
)
print(response.output_text)
```
```bash
curl https://api.openai.com/v1/responses \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-5.5",
"instructions": "'"$(< prompt.txt)"'",
"input": "How would I declare a variable for a last name?"
}'
```
#### 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](https://developers.openai.com/api/docs/api-reference/chat) or [Responses](https://developers.openai.com/api/docs/api-reference/responses). This enables you to maximize cost and latency savings from [prompt caching](https://developers.openai.com/api/docs/guides/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](https://developers.openai.com/api/docs/guides/model-optimization) 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.
```
# 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