FetchPrompt Team • 15 Jan 2026

Using a REST API for Prompt Retrieval in AI Applications

When prompts are managed externally, your application needs a reliable way to fetch them at runtime. A REST API is the most straightforward and language-agnostic approach — it works with any programming language, framework, or deployment environment.

Why REST for Prompt Retrieval?

Language Agnostic

Whether your application is built with Python, JavaScript, Go, Rust, or any other language, it can call a REST API. No SDKs to install, no dependencies to manage, no version conflicts.

Familiar Pattern

Every developer knows how to make HTTP requests. There's no learning curve. The integration looks like any other API call in your codebase.

Simple Integration

A prompt fetch is a single GET request. It returns JSON. Your application reads the content field and passes it to the LLM. The integration is typically 5-10 lines of code.

Infrastructure Ready

REST APIs work with existing infrastructure: load balancers, CDNs, API gateways, monitoring tools, and caching layers all work out of the box.

Basic Integration

Here's how a typical prompt fetch looks:

JavaScript / TypeScript

async function getPrompt(slug, variables = {}) {
  const params = new URLSearchParams(variables);
  const response = await fetch(
    `https://api.fetchprompt.com/v1/prompts/${slug}?${params}`,
    {
      headers: {
        Authorization: `Bearer ${process.env.FETCHPROMPT_API_KEY}`,
      },
    }
  );
  const data = await response.json();
  return data.data.content;
}

// Usage
const prompt = await getPrompt("customer-support", {
  user_name: "Alice",
  issue_type: "billing",
});

Python

import requests
import os

def get_prompt(slug, variables=None):
    response = requests.get(
        f"https://api.fetchprompt.com/v1/prompts/{slug}",
        params=variables or {},
        headers={
            "Authorization": f"Bearer {os.environ['FETCHPROMPT_API_KEY']}"
        },
    )
    data = response.json()
    return data["data"]["content"]

# Usage
prompt = get_prompt("customer-support", {
    "user_name": "Alice",
    "issue_type": "billing",
})

cURL

curl "https://api.fetchprompt.com/v1/prompts/customer-support\
?user_name=Alice&issue_type=billing" \
  -H "Authorization: Bearer fp_prod_xxx"

Architecture Patterns

Direct Fetch

The simplest pattern: your application fetches the prompt directly before each LLM call.

Application → FetchPrompt API → Application → LLM

This is the right starting point for most applications. It's simple, reliable, and easy to debug.

Fetch with Caching

For high-traffic applications, add a cache layer to reduce API calls:

Application → Cache → (miss) → FetchPrompt API
                   → (hit)  → Return cached prompt

FetchPrompt supports ETag-based caching. Your application can send the ETag from a previous response, and if the prompt hasn't changed, the API returns a 304 Not Modified — saving bandwidth and latency.

Background Refresh

For latency-sensitive applications, fetch prompts on a schedule rather than per-request:

Background job → FetchPrompt API → Update local cache
Application → Local cache → Use prompt

This pattern ensures your application always has prompts available without adding latency to the request path.

Handling Errors Gracefully

Your prompt fetch should never crash your application. Here are the patterns for robust error handling:

Fallback to Cache

If the API is unreachable, use the last known version from your cache:

async function getPromptWithFallback(slug, variables) {
  try {
    const prompt = await getPrompt(slug, variables);
    cache.set(slug, prompt);
    return prompt;
  } catch (error) {
    const cached = cache.get(slug);
    if (cached) return cached;
    throw error;
  }
}

Circuit Breaker

If the API has repeated failures, stop calling it temporarily to avoid cascading issues:

let failures = 0;
const THRESHOLD = 3;
const RESET_MS = 60000;

async function getPromptWithBreaker(slug, variables) {
  if (failures >= THRESHOLD) {
    return cache.get(slug); // Use cache during circuit break
  }
  try {
    const prompt = await getPrompt(slug, variables);
    failures = 0;
    return prompt;
  } catch (error) {
    failures++;
    setTimeout(() => (failures = 0), RESET_MS);
    return cache.get(slug);
  }
}

Authentication

API requests are authenticated using API keys passed in the Authorization header:

Authorization: Bearer fp_prod_xxx

Key principles:

  • One key per environment: Staging keys fetch staging prompts, production keys fetch production prompts
  • Store keys securely: Use environment variables, not hardcoded strings
  • Rotate regularly: Revoke and regenerate keys on a schedule
  • Separate by service: Different services should use different API keys for isolation and auditing

Performance Considerations

Keep Prompts Reasonably Sized

Smaller prompts transfer faster. If you have very large prompts, consider whether all that content needs to be in the prompt or if some of it can be injected by the application.

Use ETag Caching

ETags let the server tell your application "nothing has changed" without re-sending the full prompt content. This is especially valuable for prompts that are fetched frequently but updated rarely.

Monitor Latency

Track the p50 and p99 latency of your prompt fetches. If latency is a concern, consider the background refresh pattern described above.

FetchPrompt's API

FetchPrompt provides a REST API designed for prompt retrieval:

  • GET /v1/prompts/{slug} — Fetch a prompt with optional variable interpolation
  • ETag support for efficient caching
  • Environment scoping via API key (staging vs. production)
  • Rate limiting to protect your usage quota
  • Edge deployment for low-latency responses globally

The API returns JSON with the interpolated prompt content, version number, and metadata — everything your application needs to use the prompt immediately.

REST APIArchitectureIntegration