Best practices for session management with the Prompt API

Thomas Steiner

Published: January 27, 2025

Explainer	Web	Extensions	Chrome Status	Intent
GitHub	In EPP	Origin trial	Not applicable	Not applicable

Note: The Prompt API is a built-in AI API that the Chrome team is exploring. You can test it locally with your apps by joining the Early Preview Program, or in production in your Chrome Extensions by signing up for the Prompt API origin trial for Chrome Extensions.

One key feature of the Prompt API are sessions. They let you have one or multiple ongoing conversations with the AI model, without the model losing track of the context of what was said. This guide introduces best practices for session management with the language model.

You may want to engage in session management for one or more parallel sessions if you're building a classic chatbot, where one user interacts with AI. Or, if you have a customer relationship management systems where one support agent deals with multiple customers in parallel and makes use of AI to help the support agent keep track of the various conversations.

Initialize a session with a system prompt

A system prompt sets up the context of the session at the start. For example, you can use the system prompt to tell the model how it should respond.

// Make this work in web apps and in extensions.
const aiNamespace = self.ai || chrome.aiOriginTrial || chrome.ai;
const languageModel = await aiNamespace.languageModel.create({
  systemPrompt: 'You are a helpful assistant and you speak like a pirate.',
});
console.log(await languageModel.prompt('Tell me a joke.'));
// 'Avast ye, matey! What do you call a lazy pirate?\n\nA **sail-bum!**\n\nAhoy
// there, me hearties!  Want to hear another one? \n'

Clone a main session

If you want to start a new session after a session ends, or if you want to have multiple independent conversations in parallel, you can clone a main session.

The clone inherits session parameters, such as temperature or topK, and any session interaction history. This is useful if, for example, you initialized the main session with a system prompt. This way, your app only needs to do this work once—all clones inherit the system prompt from the main session.

// Make this work in web apps and in extensions.
const aiNamespace = self.ai || chrome.aiOriginTrial || chrome.ai;
const languageModel = await aiNamespace.languageModel.create({
  systemPrompt: 'You are a helpful assistant and you speak like a pirate.',
});

// The original session `languageModel` remains unchanged, and
// the two clones can be interacted with independently from each other.
const firstClonedLanguageModel = await languageModel.clone();
const secondClonedLanguageModel = await languageModel.clone();
// Interact with the sessions independently.
await firstClonedLanguageModel.prompt('Tell me a joke about parrots.');
await secondClonedLanguageModel.prompt('Tell me a joke about treasure troves.');
// Each session keeps its own context.
// The first session's context is jokes about parrots.
await firstClonedLanguageModel.prompt('Tell me another.');
// The second session's context is jokes about treasure troves.
await secondClonedLanguageModel.prompt('Tell me another.');

Restore a past session

With initial prompts, you can prime the model with a set of example prompts and responses, to generate better results. This is often used in n-shot prompting, to create responses that mirror your expectations.

Note: There's a feature request for proper session restoring, but until then, this creative usage of initial prompts works.

If you keep track of ongoing conversations with the model, you can use this practice to restore a session. For example, after a browser restarts, you can help your user continue engaging with the model from where they left off. One approach is to keep track of session history in local storage.

// Make this work in web apps and in extensions.
const aiNamespace = self.ai || chrome.aiOriginTrial || chrome.ai;

// Restore the session from localStorage, or initialize a new session.
// The UUID is hardcoded here, but would come from a
// session picker in your user interface.
const uuid = '7e62c0e0-6518-4658-bc38-e7a43217df87';

function getSessionData(uuid) {
  try {
    const storedSession = localStorage.getItem(uuid);
    return storedSession ? JSON.parse(storedSession) : false;
  } catch {
    return false;
  }
}

let sessionData = getSessionData(uuid);

// Initialize a new session.
if (!sessionData) {
  // Get the current default parameters so they can be restored as they were,
  // even if the default values change in the future.
  const { defaultTopK, defaultTemperature } =
    await aiNamespace.languageModel.capabilities();
  sessionData = {
    systemPrompt: '',
    initialPrompts: [],
    topK: defaultTopK,
    temperature: defaultTemperature,
  };
}

// Initialize the session with the (previously stored or new) session data.
const languageModel = await aiNamespace.languageModel.create(sessionData);

// Keep track of the ongoing conversion and store it in localStorage.
const prompt = 'Tell me a joke';
try {
  const stream = languageModel.promptStreaming(prompt);
  let result = '';
  // You can already work with each `chunk`, but then store
  // the final `result` in history.
  for await (const chunk of stream) {
    // In practice, you'd render the chunk.
    console.log(chunk);
    result = chunk;
  }

  sessionData.initialPrompts.push(
    { role: 'user', content: prompt },
    { role: 'assistant', content: result },
  );

  // To avoid growing localStorage infinitely, make sure to delete
  // no longer used sessions from time to time.
  localStorage.setItem(uuid, JSON.stringify(sessionData));
} catch (err) {
  console.error(err.name, err.message);
}

Tip: Use the Summarizer API initialized with a type: 'headline' options field to automatically give each session a human-readable session title.

Preserve session quota by letting the user stop the model

Each session has a context window that you can see by accessing the session's relevant fields maxTokens, tokensLeft, and tokensSoFar.

const { maxTokens, tokensLeft, tokensSoFar } = languageModel;

When this context window is exceeded, it causes the session to lose track of the oldest messages, which can be undesirable because this context may have been important. To preserve quota, if after submitting a prompt the user sees that an answer isn't going to be useful, allow them to stop the language model from answering by making use of the AbortController.

Both the prompt() and the promptStreaming() methods accept an optional second parameter with a signal field, to allow the user to stop the session.

const controller = new AbortController();
stopButton.onclick = () => controller.abort();

try {
  const stream = languageModel.promptStreaming('Write me a poem!', {
    signal: controller.signal,
  });
  for await (const chunk of stream) {
    console.log(chunk);
  }
} catch (err) {
  // Ignore `AbortError` errors.
  if (err.name !== 'AbortError') {
    console.error(err.name, err.message);
  }
}

Demo

See AI session management in action in the AI session management demo. Create multiple parallel conversations with the Prompt API, reload the tab or even restart your browser, and continue where you left off. See the source code on GitHub.

Conclusions

By thoughtfully managing AI sessions with these techniques and best practices, you can unlock the full potential of the Prompt API, delivering more efficient, responsive, and user-centric applications. You can also combine these approaches, for example, by letting the user clone a restored past session, so they can run "what if" scenarios.

Acknowledgements

This guide was reviewed by Sebastian Benz, Andre Bandarra, François Beaufort, and Alexandra Klepper.