Generative AI attributes

Understand the key attributes that your generative AI app sends to Axiom.

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).

What’s next?

After capturing and analyzing production telemetry:

• Visualize traces in Console.
• Use the new insights to iterate on your capability.