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Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add github/awesome-copilot --skill arize-prompt-optimization -a cursor -y
Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.
Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.
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.
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 |
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.attributes.input.value contains the user's question. The actual LLM prompt lives on child LLM spans -- navigate down the trace tree.attributes.input.value has tool input, attributes.output.value has tool result. Not typically where prompts live.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 |
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.md401 Unauthorized / missing API key → run ax profiles show to inspect the current profile. If the profile is missing or the API key is wrong, follow references/ax-profiles.md to create/update it. If the user doesn't have their key, direct them to https://app.arize.com/admin > API Keysax spaces list to pick by name, or ask the userax projects list -o json --limit 100 and present as selectable optionsax ai-integrations list --space SPACE to check for platform-managed credentials. If none exist, ask the user to provide the key or create an integration via the arize-ai-provider-integration skill.env files or search the filesystem for credentials. Use ax profiles for Arize credentials and ax ai-integrations for LLM provider keys. If credentials are not available through these channels, ask the user.# 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 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 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
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.
# 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
# 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
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 < threshold)
jq '[.[] | select(.evaluations.correctness.score < 0.5)]' experiment_*/runs.json
Look for patterns across failures:
eval.*.explanation tells you WHY something failedUse 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
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
After the LLM returns the revised messages array:
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
# 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
ax experiments export EXP_A and ax experiments export EXP_BApply 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'" |
When optimizing prompts that use template variables:
{variable}): Python f-string / Jinja style. Most common in Arize.{{variable}}): Mustache style. Used when the framework requires it.ax traces list PROJECT --filter "status_code = 'ERROR'" --limit 5
ax spans export PROJECT --trace-id TRACE_ID
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
ax datasets list --space SPACE
ax experiments list --dataset DATASET_NAME --space SPACE
ax datasets export DATASET_NAME --space SPACE
ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE
ax spans export PROJECT \
--filter "attributes.openinference.span.kind = 'LLM' AND annotation.format.label = 'incorrect'" \
-l 10 --stdout > bad_format.json
ax spans export PROJECT \
--filter "annotation.faithfulness.label = 'unfaithful'" \
-l 20 --stdout
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
| Problem | Solution |
|---|---|
ax: command not found | See references/ax-setup.md |
No profile found | No profile is configured. See references/ax-profiles.md to create one. |
No input_messages on span | Check span kind -- Chain/Agent spans store prompts on child LLM spans, not on themselves |
Prompt template is null | Not all instrumentations emit prompt_template. Use input_messages or input.value instead |
| Variables lost after optimization | Verify the revised prompt preserves all {var} placeholders from the original |
| Optimization makes things worse | Check for overfitting -- the meta-prompt may have memorized test data. Ensure few-shot examples are synthetic |
| No eval/annotation columns | Run evaluations first (via Arize UI or SDK), then re-export |
| Experiment output column not found | The column name is {experiment_name}.output -- check exact experiment name via ax experiments get |
jq errors on span JSON | Ensure you're targeting the correct file path (e.g., trace_*/spans.json) |