arize-prompt-optimization
Arize Prompt Optimization Skill
SPACE— All--spaceflags and theARIZE_SPACEenv var accept a space name (e.g.,my-workspace) or a base64 space ID (e.g.,U3BhY2U6...). Find yours withax spaces list.
Concepts
Where Prompts Live in Trace Data
LLM applications emit spans following OpenInference semantic conventions. Prompts are stored in different span attributes depending on the span kind and instrumentation:
| Column | What it contains | When to use |
|---|---|---|
attributes.llm.input_messages |
Structured chat messages (system, user, assistant, tool) in role-based format | Primary source for chat-based LLM prompts |
attributes.llm.input_messages.roles |
Array of roles: system, user, assistant, tool |
Extract individual message roles |
attributes.llm.input_messages.contents |
Array of message content strings | Extract message text |
attributes.input.value |
Serialized prompt or user question (generic, all span kinds) | Fallback when structured messages are not available |
attributes.llm.prompt_template.template |
Template with {variable} placeholders (e.g., "Answer {question} using {context}") |
When the app uses prompt templates |
attributes.llm.prompt_template.variables |
Template variable values (JSON object) | See what values were substituted into the template |
attributes.output.value |
Model response text | See what the LLM produced |
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