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After you instrument your app, every LLM call sends a detailed span to your Axiom dataset. The spans are enriched with standardized gen_ai.* attributes that make your AI interactions easy to query and analyze. Key attributes include the following:
  • gen_ai.capability.name: The high-level capability name you defined in withSpan.
  • gen_ai.step.name: The specific step within the capability.
  • gen_ai.request.model: The model requested for the completion.
  • gen_ai.response.model: The model that actually fulfilled the request.
  • gen_ai.usage.input_tokens: The number of tokens in the prompt.
  • gen_ai.usage.output_tokens: The number of tokens in the generated response.
  • gen_ai.prompt: The full, rendered prompt or message history sent to the model (as a JSON string).
  • gen_ai.completion: The full response from the model, including tool calls (as a JSON string).
  • gen_ai.response.finish_reasons: The reason the model stopped generating tokens. For example: stop, tool-calls.
  • gen_ai.tool.name: The name of the executed tool.
  • gen_ai.tool.arguments: The arguments passed to the tool (as a JSON string).
  • gen_ai.tool.message: The result returned by the tool (as a JSON string).

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What’s next?

After capturing and analyzing production telemetry: