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Documentation Index

Fetch the complete documentation index at: https://docs.helicone.ai/llms.txt

Use this file to discover all available pages before exploring further.

This version of prompts is deprecated. It will remain available for existing users until August 20th, 2025.

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Build and Deploy Production-Ready Prompts

The Helicone Prompt Editor enables you to:
  • Design prompts collaboratively in a UI
  • Create templates with variables and track real production inputs
  • Connect to any major AI provider (Anthropic, OpenAI, Google, Meta, DeepSeek and more)

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Version Control for Your Prompts

Take full control of your prompt versions:
  • Track versions automatically in code or manually in UI
  • Switch, promote, or rollback versions instantly
  • Deploy any version using just the prompt ID

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Prompt Editor Copilot

Write prompts faster and more efficiently:
  • Get auto-complete and smart suggestions
  • Add variables (⌘E) and XML delimiters (⌘J) with quick shortcuts
  • Perform any edits you describe with natural language (⌘K)

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Real-Time Testing

Test and refine your prompts instantly:
  • Edit and run prompts side-by-side with instant feedback
  • Experiment with different models, messages, temperatures, and parameters

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Auto-Improve (Beta)

We’re excited to launch Auto-Improve, an intelligent prompt optimization tool that helps you write more effective LLM prompts. While traditional prompt engineering requires extensive trial and error, Auto-Improve analyzes your prompts and suggests improvements instantly.

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How it Works

  1. Click the Auto-Improve button in the Helicone Prompt Editor
  2. Our AI analyzes each sentence of your prompt to understand:
    • The semantic interpretation
    • Your instructional intent
    • Potential areas for enhancement
  3. Get a new suggested optimized version of your prompt
Auto-Improve feature interface

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Key Benefits

  • Semantic Analysis: Goes beyond simple text improvements by understanding the purpose behind each instruction
  • Maintains Intent: Preserves your original goals while enhancing how they’re communicated
  • Time Saving: Skip hours of prompt iteration and testing
  • Learning Tool: Understand what makes an effective prompt by comparing your original with the improved version

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Using Prompts in Your Code

API Migration Notice: We are actively working on a new Router project that will include an updated Generate API. While the previous Generate API (legacy) is still functional (see the notice on that page for deprecation timelines), here’s a temporary way to import and use your UI-managed prompts directly in your code in the meantime:

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For OpenAI users or Azure

const openai = new OpenAI({
  baseURL: "https://generate.helicone.ai/v1",
  defaultHeaders: {
    "Helicone-Auth": `Bearer ${process.env.HELICONE_API_KEY}`,
    OPENAI_API_KEY: process.env.OPENAI_API_KEY,

    // For Azure users
    AZURE_API_KEY: process.env.AZURE_API_KEY,
    AZURE_REGION: process.env.AZURE_REGION,
    AZURE_PROJECT: process.env.AZURE_PROJECT,
    AZURE_LOCATION: process.env.AZURE_LOCATION,
  },
});

const response = await openai.chat.completions.create({
  inputs: {
    number: "world",
  },
  promptId: "helicone-test",
} as any);

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Using API to pull down the compiled prompt templates

Step 1: Get the compile the prompt template
Bash exmaple 
curl --request POST \
  --url https://api.helicone.ai/v1/prompt/helicone-test/compile \
  --header "Content-Type: application/json" \
  --header "authorization: $HELICONE_API_KEY" \
  --data '{
  "filter": "all",
  "includeExperimentVersions": false,
  "inputs": {
    "number": "10"
  }
}'
Javascript example with openai 
const promptTemplate = await fetch(
  "https://api.helicone.ai/v1/prompt/helicone-test/compile",
  {
    method: "POST",
    headers: {
      authorization: "sk-helicone-n4vqkhi-gg6exli-teictoi-aw7azyy",
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      filter: "all",
      includeExperimentVersions: false,
      inputs: { number: "10" }, // place all of your inputs here
    }),
  }
).then((res) => res.json() as any);

const example = (await openai.chat.completions.create({
  ...(promptTemplate.data.prompt_compiled as any),
  stream: false, // or true
})) as any;