Optimize prompts using custom scorers

This notebook walks you through how to make custom scorers using MLflow make_judge.

Often, built-in scorers and judges don't fit all use cases. Take advantage of custom scorers or judges to ensure you have accurate evaluations to optimize against.

The notebook walks you through a markdown judge that optimizes a prompt to output in a more markdown format.

Python

%pip install --upgrade mlflow databricks-sdk dspy openai
dbutils.library.restartPython()

Use MLflow make_judge

MLflow's recent release of make_judge allows you to make any judge catered to your specific use case.

Python

from mlflow.genai.judges import make_judge

# Create a scorer for customer support quality
markdown_output_judge = make_judge(
    name="markdown_quality",
    instructions=(
        "Evaluate if the answer in {{ outputs }} follows a markdown formatting and accurately answers the question in {{ inputs }} and matches {{ expectations }}. Rate as high, medium or low quality"
    ),
    model="databricks:/databricks-claude-sonnet-4-5"
)

Objective function to map feedback

The feedback provided by the judge needs to be mapped to a numerical number that the optimizer can use. The optimizer also incorporates the feedback from the judge.

You need a function to provide this mapping back to the optimizer.

Python

def feedback_to_score(scores: dict) -> float:
    """Convert feedback values to numerical scores."""
    feedback_value = scores["markdown_quality"]

    # Map categorical feedback to numerical values
    feedback_mapping = {
        "high": 1.0,
        "medium": 0.5,
        "low": 0.0
    }

    # Handle Feedback objects by accessing .value attribute
    if hasattr(feedback_value, 'value'):
        feedback_str = str(feedback_value.value).lower()
    else:
        feedback_str = str(feedback_value).lower()

    return feedback_mapping.get(feedback_str, 0.0)

Test the model

You can test this model as is. In the following example, the model doesn't output in a Markdown format.

Python

import mlflow
import openai
from mlflow.genai.optimize import GepaPromptOptimizer
from databricks_openai import DatabricksOpenAI

# Change this to your workspace catalog and schema
catalog = ""
schema = ""
prompt_location = f"{catalog}.{schema}.markdown"

openai_client = DatabricksOpenAI()

# Register initial prompt
prompt = mlflow.genai.register_prompt(
    name=prompt_location,
    template="Answer this question: {{question}}",
)

# Define your prediction function
def predict_fn(question: str) -> str:
    prompt = mlflow.genai.load_prompt(f"prompts:/{prompt_location}/1")
    completion = openai_client.chat.completions.create(
        model="databricks-gpt-oss-20b",
        messages=[{"role": "user", "content": prompt.format(question=question)}],
    )
    return completion.choices[0].message.content

Python

from IPython.display import Markdown

output = predict_fn("What is the capital of France?")

Markdown(output[1]['text'])

Run the optimizer

Some example data has been provided for you.

Python

# Training data with inputs and expected outputs
dataset = [
    {
        # The inputs schema should match with the input arguments of the prediction function.
        "inputs": {"question": "What is the capital of France?"},
        "expectations": {"expected_response": """## Paris - Capital of France

**Paris** is the capital and largest city of France, located in the *north-central* region.

### Key Facts:
- **Population**: ~2.2 million (city), ~12 million (metro area)
- **Founded**: 3rd century BC
- **Nickname**: *"City of Light"* (La Ville Lumière)

### Notable Landmarks:
1. **Eiffel Tower** - Iconic iron lattice tower
2. **Louvre Museum** - World's largest art museum
3. **Notre-Dame Cathedral** - Gothic masterpiece
4. **Arc de Triomphe** - Monument honoring French soldiers

> Paris is not only the political center but also a global hub for art, fashion, and culture."""},
    },
    {
        "inputs": {"question": "What is the capital of Germany?"},
        "expectations": {"expected_response": """## Berlin - Capital of Germany

**Berlin** is Germany's capital and largest city, situated in the *northeastern* part of the country.

### Historical Significance:
| Period | Importance |
|--------|------------|
| 1961-1989 | Divided by the **Berlin Wall** |
| 1990 | Reunification capital |
| Present | Political & cultural center |

### Must-See Attractions:
1. **Brandenburg Gate** - Neoclassical monument
2. **Reichstag Building** - Seat of German Parliament
3. **Museum Island** - UNESCO World Heritage site
4. **East Side Gallery** - Open-air gallery on Berlin Wall remnants

> *"Ich bin ein Berliner"* - Famous quote by JFK highlighting Berlin's symbolic importance during the Cold War."""},
    },
    {
        "inputs": {"question": "What is the capital of Japan?"},
        "expectations": {"expected_response": """## Tokyo (東京) - Capital of Japan

**Tokyo** is the capital of Japan and the world's most populous metropolitan area, located on the *eastern coast* of Honshu island.

### Demographics & Economy:
- **Population**: ~14 million (city), ~37 million (Greater Tokyo Area)
- **GDP**: One of the world's largest urban economies
- **Status**: Global financial hub and technology center

### Districts & Landmarks:
1. **Shibuya** - Famous crossing and youth culture
2. **Shinjuku** - Business district with Tokyo Metropolitan Government Building
3. **Asakusa** - Historic area with *Sensō-ji Temple*
4. **Akihabara** - Electronics and anime culture hub

### Cultural Blend:
- Ancient temples ⛩️ alongside futuristic skyscrapers 🏙️
- Traditional tea ceremonies 🍵 and cutting-edge technology 🤖

> Tokyo seamlessly combines **centuries-old traditions** with *ultra-modern innovation*, making it a unique global metropolis."""},
    },
    {
        "inputs": {"question": "What is the capital of Italy?"},
        "expectations": {"expected_response": """## Rome (Roma) - The Eternal City

**Rome** is the capital of Italy, famously known as *"The Eternal City"* (*La Città Eterna*), with over **2,750 years** of history.

### Historical Timeline:


753 BC → Founded (according to legend)
27 BC → Capital of Roman Empire
1871 → Capital of unified Italy
Present → Modern capital with ancient roots



### UNESCO World Heritage Sites:
1. **The Colosseum** - Ancient amphitheater (80 AD)
2. **Roman Forum** - Center of ancient Roman life
3. **Pantheon** - Best-preserved ancient Roman building
4. **Vatican City** - Independent city-state within Rome
   - *St. Peter's Basilica*
   - *Sistine Chapel* (Michelangelo's ceiling)

### Famous Quote:
> *"All roads lead to Rome"* - Ancient proverb reflecting Rome's historical importance as the center of the Roman Empire

### Cultural Significance:
- Birthplace of **Western civilization**
- Center of the *Catholic Church*
- Home to countless masterpieces of ***Renaissance art and architecture***"""},
    },
]

# Optimize the prompt
result = mlflow.genai.optimize_prompts(
    predict_fn=predict_fn,
    train_data=dataset,
    prompt_uris=[prompt.uri],
    optimizer=GepaPromptOptimizer(reflection_model="databricks:/databricks-claude-sonnet-4-5"),
    scorers=[markdown_output_judge],
    aggregation=feedback_to_score
)

# Use the optimized prompt
optimized_prompt = result.optimized_prompts[0]
print(f"Optimized template: {optimized_prompt.template}")

Review your prompt

Open the link to your MLflow experiment and complete the following steps to have the prompts appear in your experiment:

1. Ensure your experiment type is set to AI apps and agents.
2. Navigate to the prompt tab.
3. Click select a schema on the top right and enter the same schema you set above to see your prompt.

Load the new prompt and test again

Review what the prompt looks like and load it into your predict function to see how differently the model performs.

Python

from IPython.display import Markdown
prompt = mlflow.genai.load_prompt(f"prompts:/{prompt_location}/10")

Markdown(prompt.template)

Python

from IPython.display import Markdown

def predict_fn(question: str) -> str:
    prompt = mlflow.genai.load_prompt(f"prompts:/{prompt_location}/10")
    completion = openai_client.chat.completions.create(
        model="databricks-gpt-oss-20b",
        # load prompt template using PromptVersion.format()
        messages=[{"role": "user", "content": prompt.format(question=question)}],
    )
    return completion.choices[0].message.content

output = predict_fn("What is the capital of France?.")

Markdown(output[1]['text'])

Example notebook

The following is a runnable notebook that shows prompt opitimization using custom scorers.

Prompt opitimization using custom scorers

Open notebook in new tabOpen in Databricks