Examples

Real-world examples showing how teams use PromptOps to manage their AI prompts in production.

Customer Support Agent

optimizebenchmark

Optimize a support agent prompt for accuracy on real customer tickets. Benchmark across Claude, GPT-4o, and Gemini.

const result = await promptops.optimize({
  prompt: "support-agent",
  dataset: "customer-tickets-q4",
  objective: "accuracy",
  iterations: 50
});
// accuracy: 0.73 → 0.91

Code Review Bot

registerdeploy

Register a code review prompt that catches bugs and suggests improvements. Deploy with canary rollouts.

await promptops.register({
  name: "code-reviewer",
  model: "claude-sonnet",
  prompt: reviewPrompt,
  tags: ["engineering", "ci"]
});

Data Extraction Pipeline

benchmark

Benchmark extraction accuracy across models to find the best cost/performance ratio for structured data tasks.

const results = await promptops.benchmark({
  prompt: "invoice-extractor",
  models: ["gpt-4o", "claude-sonnet", "gemini-pro"],
  dataset: "invoices-test-set",
  metrics: ["accuracy", "cost"]
});

Have an example to share? Open a PR on GitHub