Local prompt testing allows you to evaluate custom prompt implementations using the Yields Output function. This approach is ideal when you want to test your own prompt logic, integrate with specific LLM providers, or implement complex prompt workflows.

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Basic Local Prompt Testing

Use the Yields Output function to define custom prompt logic that will be executed for each test case:

from maxim import Maxim
from maxim.models import (
    YieldedOutput,
    YieldedOutputMeta,
    YieldedOutputTokenUsage,
    YieldedOutputCost,
)
import openai
import time

# Initialize Maxim and OpenAI

maxim = Maxim({"api_key": "your-maxim-api-key"})

client = openai.OpenAI(api_key="your-openai-api-key")


def custom_prompt_function(data):
    """Custom prompt implementation with OpenAI"""

    # Define your prompt template
    system_prompt = "You are a helpful assistant that explains complex topics in simple, easy-to-understand language."

    try:
        # Start timing the API call
        start_time = time.time()

        response = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": data["input"]},
            ],
            temperature=0.7,
            max_tokens=200,
        )

        # Calculate latency in milliseconds
        end_time = time.time()
        latency_ms = (end_time - start_time) * 1000

        return YieldedOutput(
            data=response.choices[0].message.content,
            meta=YieldedOutputMeta(
                cost=YieldedOutputCost(
                    input_cost=response.usage.prompt_tokens
                    * 0.0015
                    / 1000,  # GPT-3.5 pricing
                    output_cost=response.usage.completion_tokens * 0.002 / 1000,
                    total_cost=response.usage.total_tokens * 0.0015 / 1000,
                ),
                usage=YieldedOutputTokenUsage(
                    prompt_tokens=response.usage.prompt_tokens,
                    completion_tokens=response.usage.completion_tokens,
                    total_tokens=response.usage.total_tokens,
                    latency=latency_ms,
                ),
            ),
        )
    except Exception as e:
        # Handle errors gracefully
        return YieldedOutput(data=f"Error: {str(e)}")


# Run the test

result = (
    maxim.create_test_run(
        name="Local Prompt Test - Educational Content",
        in_workspace_id="your-workspace-id",
    )
    .with_data_structure({"input": "INPUT", "expected_output": "EXPECTED_OUTPUT"})
    .with_data("dataset-id")
    .with_evaluators("Bias", "Clarity")
    .yields_output(custom_prompt_function)
    .run()
)

print(f"Test completed! View results: {result.test_run_result.link}")

​
Advanced Prompt Testing with Context

You can also test prompts that use additional context or implement RAG (Retrieval-Augmented Generation):

from maxim import Maxim
from maxim.models import (
    YieldedOutput,
    YieldedOutputMeta,
    YieldedOutputTokenUsage,
    YieldedOutputCost,
)
import openai
import time

# Initialize Maxim and OpenAI

maxim = Maxim({"api_key": "your-maxim-api-key"})

client = openai.OpenAI(api_key="your-openai-api-key")


def rag_prompt_function(data):
    """Prompt function with retrieval-augmented generation"""

    # Simulate context retrieval (replace with your actual RAG logic)
    retrieved_context = f'Context for "{data["input"]}": {data["context_to_evaluate"]}'

    system_prompt = """You are a helpful assistant. Use the provided context to answer the user's question accurately.

Context: {context}

Answer the user's question based on the context provided."""

    try:
        # Start timing the API call
        start_time = time.time()

        response = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[
                {
                    "role": "system",
                    "content": system_prompt.format(context=retrieved_context),
                },
                {"role": "user", "content": data["input"]},
            ],
            temperature=0.3,
            max_tokens=200,
        )

        # Calculate latency in milliseconds
        end_time = time.time()
        latency_ms = (end_time - start_time) * 1000

        return YieldedOutput(
            data=response.choices[0].message.content,
            retrieved_context_to_evaluate=retrieved_context,  # Important for context evaluation
            meta=YieldedOutputMeta(
                cost=YieldedOutputCost(
                    input_cost=response.usage.prompt_tokens
                    * 0.0015
                    / 1000,  # GPT-3.5 pricing
                    output_cost=response.usage.completion_tokens * 0.002 / 1000,
                    total_cost=response.usage.total_tokens * 0.0015 / 1000,
                ),
                usage=YieldedOutputTokenUsage(
                    prompt_tokens=response.usage.prompt_tokens,
                    completion_tokens=response.usage.completion_tokens,
                    total_tokens=response.usage.total_tokens,
                    latency=latency_ms,
                ),
            ),
        )
    except Exception as e:
        return YieldedOutput(data=f"Error: {str(e)}")


# Test data with context evaluation

test_data_with_context = [
    {
        "input": "What is the impact of climate change on agriculture?",
        "expected_output": "Climate change affects agriculture through temperature changes and weather patterns",
        "context_to_evaluate": "Climate change impacts on farming",
    }
]

# Run test with context evaluation

result = (
    maxim.create_test_run(name="RAG Prompt Test", in_workspace_id="your-workspace-id")
    .with_data_structure(
        {
            "input": "INPUT",
            "expected_output": "EXPECTED_OUTPUT",
            "context_to_evaluate": "CONTEXT_TO_EVALUATE",  # This column's data will be used for context evaluation. It will be overwritten in case the yielded data returns back a context to evaluate
        }
    )
    .with_data(test_data_with_context)
    .with_evaluators("Bias", "Clarity", "Faithfulness")
    .yields_output(rag_prompt_function)
    .run()
)

print(f"Test completed! View results: {result.test_run_result.link}")

​
Best Practices

  1. Error Handling: Always include proper error handling in your Yields Output function
  2. Token Tracking: Include usage and cost metadata when possible for better insights
  3. Context Management: Use Retrieved Context to Evaluate when evaluating prompts that use RAG systems

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Example Repository

For more complex examples including multi-turn conversations and advanced RAG implementations, check out our cookbooks repository for python or typescript.

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Next Steps