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INVOKE THIS SKILL when optimizing, improving, or debugging LLM prompts using production trace data, evaluations, and annotations. Also use when the user wants… npx skills add https://github.com/github/awesome-copilot --skill arize-prompt-optimization Download ZIPGitHub36.2k

Arize Prompt Optimization Skill

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.

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

Phase 1: Extract the Current Prompt

Find LLM spans containing prompts

# Sample LLM spans (where prompts live)
ax spans export PROJECT --filter "attributes.openinference.span.kind = 'LLM'" -l 10 --stdout

# Filter by model
ax spans export PROJECT --filter "attributes.llm.model_name = 'gpt-4o'" -l 10 --stdout

# Filter by span name (e.g., a specific LLM call)
ax spans export PROJECT --filter "name = 'ChatCompletion'" -l 10 --stdout

Export a trace to inspect prompt structure

# Export all spans in a trace
ax spans export PROJECT --trace-id TRACE_ID

# Export a single span
ax spans export PROJECT --span-id SPAN_ID

Extract prompts from exported JSON

# 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:

[
 {"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)

# Find error spans -- these indicate prompt failures
ax spans export PROJECT \
 --filter "status_code = 'ERROR' AND attributes.openinference.span.kind = 'LLM'" \
 -l 20 --stdout

# Find spans with low eval scores
ax spans export PROJECT \
 --filter "annotation.correctness.label = 'incorrect'" \
 -l 20 --stdout

# Find spans with high latency (may indicate overly complex prompts)
ax spans export PROJECT \
 --filter "attributes.openinference.span.kind = 'LLM' AND latency_ms > 10000" \
 -l 20 --stdout

# Export error traces for detailed inspection
ax spans export PROJECT --trace-id TRACE_ID

From datasets and experiments

# Export a dataset (ground truth examples)
ax datasets export DATASET_NAME --space SPACE
# -> dataset_*/examples.json

# Export experiment results (what the LLM produced)
ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE
# -> experiment_*/runs.json

Merge dataset + experiment for analysis

Join the two files by example_id to see inputs alongside outputs and evaluations:

# Count examples and runs
jq 'length' dataset_*/examples.json
jq 'length' experiment_*/runs.json

# View a single joined record
jq -s '
 .[0] as $dataset |
 .[1][0] as $run |
 ($dataset[] | select(.id == $run.example_id)) as $example |
 {
 input: $example,
 output: $run.output,
 evaluations: $run.evaluations
 }
' dataset_*/examples.json experiment_*/runs.json

# Find failed examples (where eval score Look for patterns across failures:

 

- **Compare outputs to ground truth**: Where does the LLM output differ from expected? 

- **Read eval explanations**: `eval.*.explanation` tells you WHY something failed 

- **Check annotation text**: Human feedback describes specific issues 

- **Look for verbosity mismatches**: If outputs are too long/short vs ground truth 

- **Check format compliance**: Are outputs in the expected format?

## Phase 3: Optimize the Prompt

### The Optimization Meta-Prompt

 Use this template to generate an improved version of the prompt. Fill in the three placeholders and send it to your LLM (GPT-4o, Claude, etc.):

You are an expert in prompt optimization. Given the original baseline prompt and the associated performance data (inputs, outputs, evaluation labels, and explanations), generate a revised version that improves results.

ORIGINAL BASELINE PROMPT

{PASTE_ORIGINAL_PROMPT_HERE}

========================

PERFORMANCE DATA

The following records show how the current prompt performed. Each record includes the input, the LLM output, and evaluation feedback:

{PASTE_RECORDS_HERE}

================

HOW TO USE THIS DATA

1. Compare outputs: Look at what the LLM generated vs what was expected
2. Review eval scores: Check which examples scored poorly and why
3. Examine annotations: Human feedback shows what worked and what didn't
4. Identify patterns: Look for common issues across multiple examples
5. Focus on failures: The rows where the output DIFFERS from the expected value are the ones that need fixing

ALIGNMENT STRATEGY

• If outputs have extra text or reasoning not present in the ground truth, remove instructions that encourage explanation or verbose reasoning
• If outputs are missing information, add instructions to include it
• If outputs are in the wrong format, add explicit format instructions
• Focus on the rows where the output differs from the target -- these are the failures to fix

RULES

Maintain Structure:

• Use the same template variables as the current prompt ({var} or {{var}})
• Don't change sections that are already working
• Preserve the exact return format instructions from the original prompt

Avoid Overfitting:

• DO NOT copy examples verbatim into the prompt
• DO NOT quote specific test data outputs exactly
• INSTEAD: Extract the ESSENCE of what makes good vs bad outputs
• INSTEAD: Add general guidelines and principles
• INSTEAD: If adding few-shot examples, create SYNTHETIC examples that demonstrate the principle, not real data from above

Goal: Create a prompt that generalizes well to new inputs, not one that memorizes the test data.

OUTPUT FORMAT

Return the revised prompt as a JSON array of messages:

[ {"role": "system", "content": "..."}, {"role": "user", "content": "..."} ]

Also provide a brief reasoning section (bulleted list) explaining:

• What problems you found
• How the revised prompt addresses each one

### Preparing the performance data

 Format the records as a JSON array before pasting into the template:

From dataset + experiment: join and select relevant columns

jq -s ' .[0] as $ds | [.[1][] | . as $run | ($ds[] | select(.id == $run.example_id)) as $ex | { input: $ex.input, expected: $ex.expected_output, actual_output: $run.output, eval_score: $run.evaluations.correctness.score, eval_label: $run.evaluations.correctness.label, eval_explanation: $run.evaluations.correctness.explanation } ] ' dataset_/examples.json experiment_/runs.json

From exported spans: extract input/output pairs with annotations

jq '[.[] | select(.attributes.openinference.span.kind == "LLM") | { input: .attributes.input.value, output: .attributes.output.value, status: .status_code, model: .attributes.llm.model_name }]' trace_*/spans.json

### Applying the revised prompt

 After the LLM returns the revised messages array:

 

- Compare the original and revised prompts side by side 

- Verify all template variables are preserved 

- Check that format instructions are intact 

- Test on a few examples before full deployment

## Phase 4: Iterate

### The optimization loop

1. Extract prompt -> Phase 1 (once)
2. Run experiment -> ax experiments create ...
3. Export results -> ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE
4. Analyze failures -> jq to find low scores
5. Run meta-prompt -> Phase 3 with new failure data
6. Apply revised prompt
7. Repeat from step 2

### Measure improvement

Compare scores across experiments

Experiment A (baseline)

jq '[.[] | .evaluations.correctness.score] | add / length' experiment_a/runs.json

Experiment B (optimized)

jq '[.[] | .evaluations.correctness.score] | add / length' experiment_b/runs.json

Find examples that flipped from fail to pass

jq -s ' [.[0][] | select(.evaluations.correctness.label == "incorrect")] as $fails | [.[1][] | select(.evaluations.correctness.label == "correct") | select(.example_id as $id | $fails | any(.example_id == $id)) ] | length ' experiment_a/runs.json experiment_b/runs.json

### A/B compare two prompts

 

- Create two experiments against the same dataset, each using a different prompt version 

- Export both: `ax experiments export EXP_A` and `ax experiments export EXP_B` 

- Compare average scores, failure rates, and specific example flips 

- Check for regressions -- examples that passed with prompt A but fail with prompt B

## Prompt Engineering Best Practices

Apply these when writing or revising prompts:

 Technique When to apply Example 
 Clear, detailed instructions Output is vague or off-topic "Classify the sentiment as exactly one of: positive, negative, neutral" 
 Instructions at the beginning Model ignores later instructions Put the task description before examples 
 Step-by-step breakdowns Complex multi-step processes "First extract entities, then classify each, then summarize" 
 Specific personas Need consistent style/tone "You are a senior financial analyst writing for institutional investors" 
 Delimiter tokens Sections blend together Use `---`, `###`, or XML tags to separate input from instructions 
 Few-shot examples Output format needs clarification Show 2-3 synthetic input/output pairs 
 Output length specifications Responses are too long or short "Respond in exactly 2-3 sentences" 
 Reasoning instructions Accuracy is critical "Think step by step before answering" 
 "I don't know" guidelines Hallucination is a risk "If the answer is not in the provided context, say 'I don't have enough information'" 
 

### Variable preservation

 When optimizing prompts that use template variables:

 

- **Single braces** (`{variable}`): Python f-string / Jinja style. Most common in Arize. 

- **Double braces** (`{{variable}}`): Mustache style. Used when the framework requires it. 

- Never add or remove variable placeholders during optimization 

- Never rename variables -- the runtime substitution depends on exact names 

- If adding few-shot examples, use literal values, not variable placeholders

## Workflows

### Optimize a prompt from a failing trace

 

- Find failing traces:

ax traces list PROJECT --filter "status_code = 'ERROR'" --limit 5

 

- Export the trace:

ax spans export PROJECT --trace-id TRACE_ID

 

- Extract the prompt from the LLM span:

jq '[.[] | select(.attributes.openinference.span.kind == "LLM")][0] | { messages: .attributes.llm.input_messages, template: .attributes.llm.prompt_template, output: .attributes.output.value, error: .attributes.exception.message }' trace_*/spans.json

 

- Identify what failed from the error message or output 

- Fill in the optimization meta-prompt (Phase 3) with the prompt and error context 

- Apply the revised prompt 

### Optimize using a dataset and experiment

 

- Find the dataset and experiment:

ax datasets list --space SPACE ax experiments list --dataset DATASET_NAME --space SPACE

 

- Export both:

ax datasets export DATASET_NAME --space SPACE ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE

 

- Prepare the joined data for the meta-prompt 

- Run the optimization meta-prompt 

- Create a new experiment with the revised prompt to measure improvement 

### Debug a prompt that produces wrong format

 

- Export spans where the output format is wrong:

ax spans export PROJECT
--filter "attributes.openinference.span.kind = 'LLM' AND annotation.format.label = 'incorrect'"
-l 10 --stdout > bad_format.json

 

- Look at what the LLM is producing vs what was expected 

- Add explicit format instructions to the prompt (JSON schema, examples, delimiters) 

- Common fix: add a few-shot example showing the exact desired output format 

### Reduce hallucination in a RAG prompt

 

- Find traces where the model hallucinated:

ax spans export PROJECT
--filter "annotation.faithfulness.label = 'unfaithful'"
-l 20 --stdout

 

- Export and inspect the retriever + LLM spans together:

ax spans export PROJECT --trace-id TRACE_ID jq '[.[] | {kind: .attributes.openinference.span.kind, name, input: .attributes.input.value, output: .attributes.output.value}]' trace_*/spans.json

 

- Check if the retrieved context actually contained the answer 

- Add grounding instructions to the system prompt: "Only use information from the provided context. If the answer is not in the context, say so."