arize-prompt-optimization

Files (3)

• SKILL.md
• ax-profiles.md · references/
• ax-setup.md · references/

SKILL.md (first lines)

# Arize Prompt Optimization Skill

## 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 |
| `attributes.llm.output_messages` | Structured model output (including tool calls) | Inspect tool-calling responses |

### Finding Prompts by Span Kind

- **LLM span** (`attributes.openinference.span.kind = 'LLM'`): Check `attributes.llm.input_messages` for structured chat messages, OR `attributes.input.value` for a serialized prompt. Check `attributes.llm.prompt_template.template` for the template.
- **Chain/Agent span**: `attributes.input.value` contains the user's question. The actual LLM prompt lives on **child LLM spans** -- navigate down the trace tree.
- **Tool span**: `attributes.input.value` has tool input, `attributes.output.value` has tool result. Not typically where prompts live.

### Performance Signal Columns

These columns carry the feedback data used for optimization:

| Column pattern | Source | What it tells you |
|---------------|--------|-------------------|
| `annotation.<name>.label` | Human reviewers | Categorical grade (e.g., `correct`, `incorrect`, `partial`) |
| `annotation.<name>.score` | Human reviewers | Numeric quality score (e.g., 0.0 - 1.0) |
| `annotation.<name>.text` | Human reviewers | Freeform explanation of the grade |
| `eval.<name>.label` | LLM-as-judge evals | Automated categorical assessment |
| `eval.<name>.score` | LLM-as-judge evals | Automated numeric score |
| `eval.<name>.explanation` | LLM-as-judge evals | Why the eval gave that score -- **most valuable for optimization** |
| `attributes.input.value` | Trace data | What went into the LLM |
| `attributes.output.value` | Trace data | What the LLM produced |
| `{experiment_name}.output` | Experiment runs | Output from a specific experiment |

## Prerequisites

Proceed directly with the task — run the `ax` command you need. Do NOT check versions, env vars, or profiles upfront.

If an `ax` command fails, troubleshoot based on the error:
- `command not found` or version error → see references/ax-setup.md
- `401 Unauthorized` / missing API key → run `ax profiles show` to inspect the current profile. If the profile is missing or the API key is wrong: check `.env` for `ARIZE_API_KEY` and use it to create/update the profile via references/ax-profiles.md. If `.env` has no key either, ask the user for their Arize API key (https://app.arize.com/admin > API Keys)
- Space ID unknown → check `.env` for `ARIZE_SPACE_ID`, or run `ax spaces list -o json`, or ask the user
- Project unclear → check `.env` for `ARIZE_DEFAULT_PROJECT`, or ask, or run `ax projects list -o json --limit 100` and present as selectable options
- LLM provider call fails (missing OPENAI_API_KEY / ANTHROPIC_API_KEY) → check `.env`, load if present, otherwise ask the user

## Phase 1: Extract the Current Prompt

### Find LLM spans containing prompts

```bash
# List LLM spans (where prompts live)
ax spans list PROJECT_ID --filter "attributes.openinference.span.kind = 'LLM'" --limit 10

# Filter by model
ax spans list PROJECT_ID --filter "attributes.llm.model_name = 'gpt-4o'" --limit 10

# Filter by span name (e.g., a specific LLM call)
ax spans list PROJECT_ID --filter "name = 'ChatCompletion'" --limit 10
```

### Export a trace to inspect prompt structure

```bash
# Export all spans in a trace
ax spans export --trace-id TRACE_ID --project PROJECT_ID

# Export a single span
ax spans export --span-id SPAN_ID --project PROJECT_ID
```

### Extract prompts from exported JSON

```bash
# Extract structured chat messages (system + user + assistant)
jq '.[0] | {
  messages: .attributes.llm.input_messages,
  model: .attributes.llm.model_name
}' trace_*/spans.json

# Extract the system prompt specifically
jq '[.[] | select(.attributes.llm.input_messages.roles[]? == "system")] | .[0].attributes.llm.input_messages' trace_*/spans.json

# Extract prompt template and variables
jq '.[0].attributes.llm.prompt_template' trace_*/spans.json

# Extract from input.value (fallback for non-structured prompts)
jq '.[0].attributes.input.value' trace_*/spans.json
```

### Reconstruct the prompt as messages

Once you have the span data, reconstruct the prompt as a messages array:

```json
[
  {"role": "system", "content": "You are a helpful assistant that..."},
  {"role": "user", "content": "Given {input}, answer the question: {question}"}
]
```

If the span has `attributes.llm.prompt_template.template`, the prompt uses variables. Preserve these placeholders (`{variable}` or `{{variable}}`) -- they are substituted at runtime.

## Phase 2: Gather Performance Data

### From traces (production feedback)

```bash
# Find error spans -- these indicate prompt failures
ax spans list PROJECT_ID \
  --filter "status_code = 'ERROR' AND attributes.openinference.span.kind = 'LLM'" \
  --limit 20

From HenriqueOliveiraFiap/awesome-copilot, MIT.

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