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Example Payloads

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Example 1: Math Tutoring

{
  "customId": "demo-arithmetic-001",
  "strategy": "RPM",
  "model": "gpt-4o",
  "datasetColumns": ["question", "hint"],
  "budget": 30,
  "minibatchSize": 3,
  "paretoSize": 4,
  "evaluator": "{\"model\": \"gpt-4o-mini\", \"metric\": \"exact_match\", \"threshold\": 0.95, \"partialCredit\": true}",
  "dataset": [
    {
      "input": {
        "question": "What is 17 + 26?",
        "hint": "Add tens first, then units."
      },
      "expectedOutput": "Answer: 43"
    },
    {
      "input": {
        "question": "Multiply 9 by 7.",
        "hint": "Use repeated addition if needed."
      },
      "expectedOutput": "Answer: 63"
    },
    {
      "input": {
        "question": "What is 125 - 67?",
        "hint": "Borrow carefully and explain."
      },
      "expectedOutput": "Answer: 58"
    },
    {
      "input": {
        "question": "What is 45 minus 18?",
        "hint": "Break 45 into 40 + 5."
      },
      "expectedOutput": "Answer: 27"
    },
    {
      "input": {
        "question": "Solve 12 * 14 using mental math.",
        "hint": "Split one factor into tens and ones."
      },
      "expectedOutput": "Answer: 168"
    },
    {
      "input": {
        "question": "Add 156 + 89.",
        "hint": "Round 89 to 90 first."
      },
      "expectedOutput": "Answer: 245"
    },
    {
      "input": {
        "question": "Divide 144 by 12.",
        "hint": "Think of 12 times table."
      },
      "expectedOutput": "Answer: 12"
    }
  ],
  "prompt": "You are an encouraging math tutor. Walk the student through each step, narrate your reasoning, and end with `Answer: <value>`."
}

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Example 2: Support Response

{
  "customId": "support-aurora-router",
  "strategy": "RPM",
  "model": "gpt-4o",
  "datasetColumns": ["customer_name", "product", "issue_summary"],
  "budget": 25,
  "minibatchSize": 2,
  "paretoSize": 2,
  "evaluator": "{\"model\": \"gpt-4o-mini\", \"metric\": \"coherence\"}",
  "dataset": [
    {
      "input": {
        "customer_name": "Rory Chen",
        "product": "Aurora Mesh Router",
        "issue_summary": "intermittent drop-offs whenever video calls start"
      },
      "expectedOutput": "Hi Rory, I refreshed QoS and shared call-stability steps so video calls stay stable."
    },
    {
      "input": {
        "customer_name": "Priya Patel",
        "product": "Aurora Mesh Router",
        "issue_summary": "needs parental controls ready before weekend trip"
      },
      "expectedOutput": "Hi Priya, I set up device groups so you can enable parental controls in one tap before the trip."
    },
    {
      "input": {
        "customer_name": "Damian Wright",
        "product": "Aurora Mesh Router",
        "issue_summary": "wants one-sentence status summary"
      },
      "expectedOutput": "Damian, the mesh rollout finished a week early with redundant coverage verified."
    },
    {
      "input": {
        "customer_name": "Sam Lee",
        "product": "Aurora Mesh Router",
        "issue_summary": "roaming handoff assurance needed"
      },
      "expectedOutput": "Hi Sam, roaming profile is enabled and handoff latency is under 120 ms confirmed via live session trace."
    }
  ],
  "prompt": "Write a concise, empathetic support reply for {customer_name} about their {product}. Highlight the fix for: {issue_summary}. Close with an offer to help further."
}

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Real-World Evolution Example

Here’s an actual GEPA execution showing how a prompt evolved from a simple instruction to a detailed, domain-aware template: Initial Prompt (Generation 0) - Score: 0.38 
Write a concise, empathetic support reply for {customer_name} about their {product}.
Highlight the fix for: {issue_summary}. Close with an offer to help further.
Evolved Prompt (Generation 1) - Score: 0.69 (+82% improvement) 
Write a concise, empathetic support reply for {customer_name} regarding their {product}.
The response should address the specific accomplishment or reassurance related to their
issue, {issue_summary}, as follows:

1. For status summary concerns, acknowledge the completion of a recent project,
   emphasizing achievements such as the mesh rollout completion ahead of schedule
   and verification of redundant coverage. This reflects a successful network
   deployment, providing confidence in the network's reliability.

2. For roaming handoff assurance, confirm technical specifics such as the roaming
   profile being enabled and handoff latency being under 120 ms. Add credibility by
   mentioning a live session trace to validate these assertions, offering the customer
   reassurance about the seamless technology transition.

Close the email with a personalized offer to provide further assistance if needed,
maintaining a supportive and clear tone throughout the communication.

Include sender details like your name, position, and company name for professionalism
and context.
Key Improvements Observed:
  • Structured approach with numbered sections
  • Domain-specific knowledge extracted from dataset (QoS, mesh rollout, 120ms handoff latency)
  • Concrete examples for different scenario types
  • Professional formatting requirements
  • Technical credibility elements (live session traces)
This demonstrates GEPA’s reflective learning: the final prompt contains specific knowledge and patterns extracted from evaluating the dataset examples.

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Python Example

import requests
import time
import json

api_key = '<YOUR_API_KEY>'
base_url = 'https://mainnet.dkn.dria.co/api/v0'

# Step 1: Prepare payload
payload = {
    "customId": "my-prompt-evolution",
    "strategy": "RPM",
    "model": "gpt-4o",
    "datasetColumns": ["customer_name", "product", "issue"],
    "budget": 25,
    "minibatchSize": 2,
    "paretoSize": 2,
    "evaluator": json.dumps({"model": "gpt-4o-mini", "metric": "coherence"}),
    "dataset": [
        {
            "input": {"customer_name": "Alice", "product": "Router", "issue": "connection drops"},
            "expectedOutput": "Hi Alice, I've reset your router settings..."
        },
        {
            "input": {"customer_name": "Charlie", "product": "Router", "issue": "firmware update"},
            "expectedOutput": "Hi Charlie, I've updated your router firmware..."
        },
        {
            "input": {"customer_name": "Bob", "product": "Router", "issue": "slow speed"},
            "expectedOutput": "Hi Bob, I've optimized your QoS settings..."
        },
        {
            "input": {"customer_name": "Dana", "product": "Router", "issue": "WiFi range"},
            "expectedOutput": "Hi Dana, I've adjusted your antenna settings..."
        }
    ],
    "prompt": "Write a helpful support reply for {customer_name} about their {product}: {issue}"
}

# Step 2: Start execution
resp = requests.post(f'{base_url}/gepa/start_execution',
    headers={'x-api-key': api_key, 'Content-Type': 'application/json'},
    json=payload)
resp.raise_for_status()
evolution_id = resp.json()['evolutionId']
print(f'✅ Evolution started: {evolution_id}')

# Wait for initialization
time.sleep(5)

# Step 3: Monitor progress
while True:
    status_resp = requests.get(f'{base_url}/gepa/get_single_execution/{evolution_id}',
        headers={'x-api-key': api_key})
    status_resp.raise_for_status()
    data = status_resp.json()

    if 'prompt' in data:
        print(f"Status: {data['status']} | Gen: {data['prompt']['currentGeneration']} | Score: {data['prompt']['currentPromptScore']}")
    else:
        print(f"Status: {data['status']}")

    if data['status'] in ['completed', 'failed']:
        break
    time.sleep(10)

# Step 4: Get evolved prompts
if data['status'] == 'completed':
    prompts_resp = requests.get(f'{base_url}/gepa/get_execution_prompts',
        params={'evolutionId': evolution_id, 'page': 1, 'limit': 20},
        headers={'x-api-key': api_key})
    prompts_resp.raise_for_status()

    prompts = prompts_resp.json()['prompts']
    print(f'\n✅ Evolution complete! {len(prompts)} generations')
    for p in prompts:
        print(f"\nGen {p['generation']} (score: {p['score']}):\n{p['prompt'][:200]}...")
else:
    print('❌ Evolution failed')