Gemini API: Few-shot prompting

Some prompts may need a bit more information or require a specific output schema for the LLM to understand and accomplish the requested task. In such cases, providing example questions with answers to the model may greatly increase the quality of the response.

Setup

Install the Google GenAI SDK

Install the Google GenAI SDK from npm.

$ npm install @google/genai

Setup your API key

You can create your API key using Google AI Studio with a single click.

Remember to treat your API key like a password. Don’t accidentally save it in a notebook or source file you later commit to GitHub. In this notebook we will be storing the API key in a .env file. You can also set it as an environment variable or use a secret manager.

Here’s how to set it up in a .env file:

$ touch .env
$ echo "GEMINI_API_KEY=<YOUR_API_KEY>" >> .env

Tip

Another option is to set the API key as an environment variable. You can do this in your terminal with the following command:

$ export GEMINI_API_KEY="<YOUR_API_KEY>"

Load the API key

To load the API key from the .env file, we will use the dotenv package. This package loads environment variables from a .env file into process.env.

$ npm install dotenv

Then, we can load the API key in our code:

const dotenv = require("dotenv") as typeof import("dotenv");

dotenv.config({
  path: "../../.env",
});

const GEMINI_API_KEY = process.env.GEMINI_API_KEY ?? "";
if (!GEMINI_API_KEY) {
  throw new Error("GEMINI_API_KEY is not set in the environment variables");
}
console.log("GEMINI_API_KEY is set in the environment variables");

GEMINI_API_KEY is set in the environment variables

Note

In our particular case the .env is is two directories up from the notebook, hence we need to use ../../ to go up two directories. If the .env file is in the same directory as the notebook, you can omit it altogether.

│
├── .env
└── examples
    └── prompting
        └── Few_shot_prompting.ipynb

Initialize SDK Client

With the new SDK, now you only need to initialize a client with you API key (or OAuth if using Vertex AI). The model is now set in each call.

const google = require("@google/genai") as typeof import("@google/genai");

const ai = new google.GoogleGenAI({ apiKey: GEMINI_API_KEY });

Select a model

Now select the model you want to use in this guide, either by selecting one in the list or writing it down. Keep in mind that some models, like the 2.5 ones are thinking models and thus take slightly more time to respond (cf. thinking notebook for more details and in particular learn how to switch the thiking off).

const tslab = require("tslab") as typeof import("tslab");

const MODEL_ID = "gemini-2.5-flash-preview-05-20";

Examples

const prompt_1 = `
  Sort the animals from biggest to smallest.
  Question: Sort Tiger, Bear, Dog
  Answer: Bear > Tiger > Dog
  Question: Sort Cat, Elephant, Zebra
  Answer: Elephant > Zebra > Cat
  Question: Sort Whale, Goldfish, Monkey
  Answer:
`;

const response_1 = await ai.models.generateContent({
  model: MODEL_ID,
  contents: prompt_1,
});
tslab.display.markdown(response_1.text ?? "");

Whale > Monkey > Goldfish

const prompt_2 = `
  Extract cities from text, include country they are in.
  USER: I visited Mexico City and Poznan last year
  MODEL: {"Mexico City": "Mexico", "Poznan": "Poland"}
  USER: She wanted to visit Lviv, Monaco and Maputo
  MODEL: {"Minsk": "Ukraine", "Monaco": "Monaco", "Maputo": "Mozambique"}
  USER: I am currently in Austin, but I will be moving to Lisbon soon
  MODEL:
`;

const response_2 = await ai.models.generateContent({
  model: MODEL_ID,
  contents: prompt_2,
});
tslab.display.markdown(response_2.text ?? "");

{“Austin”: “USA”, “Lisbon”: “Portugal”}

Next steps

Be sure to explore other examples of prompting in the repository. Try writing prompts about classifying your own data, or try some of the other prompting techniques such as zero-shot prompting.