arize-prompt-optimization

1# Arize Prompt Optimization Skill
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3> **`SPACE`** — All `--space` flags and the `ARIZE_SPACE` env var accept a space **name** (e.g., `my-workspace`) or a base64 space **ID** (e.g., `U3BhY2U6...`). Find yours with `ax spaces list`.
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5## Concepts
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7### Where Prompts Live in Trace Data
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9LLM applications emit spans following OpenInference semantic conventions. Prompts are stored in different span attributes depending on the span kind and instrumentation:
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11| Column | What it contains | When to use |
12|--------|-----------------|-------------|
13| `attributes.llm.input_messages` | Structured chat messages (system, user, assistant, tool) in role-based format | **Primary source** for chat-based LLM prompts |
14| `attributes.llm.input_messages.roles` | Array of roles: `system`, `user`, `assistant`, `tool` | Extract individual message roles |
15| `attributes.llm.input_messages.contents` | Array of message content strings | Extract message text |
16| `attributes.input.value` | Serialized prompt or user question (generic, all span kinds) | Fallback when structured messages are not available |
17| `attributes.llm.prompt_template.template` | Template with `{variable}` placeholders (e.g., `"Answer {question} using {context}"`) | When the app uses prompt templates |
18| `attributes.llm.prompt_template.variables` | Template variable values (JSON object) | See what values were substituted into the template |
19| `attributes.output.value` | Model response text | See what the LLM produced |
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