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.
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
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
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
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 list PROJECT_ID \ --filter "status_code = 'ERROR' AND attributes.openinference.span.kind = 'LLM'" \ --limit 20
Find spans with low eval scores
ax spans list PROJECT_ID \ --filter "annotation.correctness.label = 'incorrect'" \ --limit 20
Find spans with high latency (may indicate overly complex prompts)
ax spans list PROJECT_ID \ --filter "attributes.openinference.span.kind = 'LLM' AND latency_ms > 10000" \ --limit 20
Export error traces for detailed inspection
ax spans export --trace-id TRACE_ID --project PROJECT_ID From datasets and experiments
Export a dataset (ground truth examples)
ax datasets export DATASET_ID
-> dataset_*/examples.json
Export experiment results (what the LLM produced)
ax experiments export EXPERIMENT_ID
-> 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 < threshold)
- jq
- '[.[] | select(.evaluations.correctness.score < 0.5)]'
- experiment_*/runs.json
- Identify what to optimize
- 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_ID 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_ID --filter "status_code = 'ERROR'" --limit 5 Export the trace: ax spans export --trace-id TRACE_ID --project PROJECT_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 ax experiments list --dataset-id DATASET_ID Export both: ax datasets export DATASET_ID ax experiments export EXPERIMENT_ID 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 list PROJECT_ID \ --filter "attributes.openinference.span.kind = 'LLM' AND annotation.format.label = 'incorrect'" \ --limit 10 -o json
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 list PROJECT_ID \ --filter "annotation.faithfulness.label = 'unfaithful'" \ --limit 20 Export and inspect the retriever + LLM spans together: ax spans export --trace-id TRACE_ID --project PROJECT_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." Troubleshooting 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 )