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Basic monitoring is only available for prompts retrieved directly from Basalt using the get method. This approach is designed for simple use cases where you’re using a single prompt and not complex workflows. For advanced monitoring scenarios, please refer to the Tracing documentation.

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Basic Usage

When you retrieve a prompt using the SDK, it automatically includes a generation object that can be used to monitor the complete interaction. Here’s how to use it: 
// Step 1: Get the prompt with monitoring enabled
const { value, generation } = await basalt.prompt.get('my-prompt-slug')

// Step 2: Use the prompt with your LLM provider
const output = await myLLMProvider.complete(value.text)

// Step 3: Record the completion by calling end() with the output
generation.end(output)
This simple pattern automatically tracks:

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User and Organization Identification

You can associate the generation with a specific user and organization by using the identify method: 
// Identify the user and organization for this generation
generation.trace.identify({
  user: {
    id: '[email protected]',
    name: 'User Name'
  },
  organization: {
    id: 'org_123',
    name: 'Organization Name'
  }
})

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Evaluation

You can add automatic evaluators to assess the quality of the LLM output: 
// Add an evaluator to automatically assess the response quality
generation.addEvaluator({
  slug: 'good-answer'
})
Evaluators run automatically when generation.end() is called. For more information on available evaluators and how to create custom ones, see the Evaluation documentation.

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Evaluation Configuration

By default, evaluations are run on 10% of logs. You can configure the evaluation sample rate to control how often evaluations are run. 
// Configure evaluations at the trace level
generation.trace.setEvaluationConfig({
  sampleRate: 0.5  // Run evaluations on 50% of traces
})

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Complete Example

Here’s a complete example showing how to use basic monitoring with variables, user identification, and automatic evaluation: 
const result = await basalt.prompt.get('comment-classifier', {
  variables: {
    comment: 'I would like to contribute to this issue'
  }
})

if (result.error) {
  console.error(result.error)
  return
}

const { generation } = result

// Identify the user and organization
generation.trace.identify({
  user: {
    id: '[email protected]',
    name: 'Guillaume Marquis'
  },
  organization: {
    id: 'org_123',
    name: 'Basalt'
  }
})

// Add an automatic evaluator
generation.trace.addEvaluator({
  slug: 'good-answer'
})

// Call your LLM provider (commented out in this example)
// const completion = await openai.chat.completions.create({
//   model: 'gpt-4o',
//   messages: [{ role: 'user', content: 'hey' }]
// })

// Record the LLM output
generation.end('output')

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Advanced Use Cases

Basic monitoring is suitable for simple scenarios where you’re using a single prompt. For more advanced use cases, such as: Please refer to the Tracing documentation, which provides comprehensive tools for these scenarios.