Nomadic’s prompt tuning capabilities enable you to discover and capture nuanced prompt requirements for optimal performance.

When building LLM applications, it’s crucial to recognize that some prompts are more performant than others. MLEs often experiment with hundreds of prompts before discovering optimal prompts. Nomadic makes it easy for you to manage, experiment, and set the best prompting approaches within your system.

Below is a basic example of how you can use Nomadic’s SDK to systematically test different prompt approaches (technique, complexity, and task focus) to discover the best-performing prompt approach.

Nomadic Workspace (GUI) prompt playground coming soon!

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1. Import Nomadic libraries

prompt_optimizer.py
import os
from nomadic.experiment import Experiment
from nomadic.model import OpenAIModel
from nomadic.tuner import tune
from llama_index.core.evaluation import SemanticSimilarityEvaluator
from llama_index.embeddings.openai import OpenAIEmbedding

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2. Set up your project directory and API keys

Make sure you’ve specified the API key to access your model (e.g. OpenAI, Mistral) in {PROJECT_DIRECTORY}/.env.dev. Nomadic will then establish the connection to your model endpoints.

prompt_optimizer.py
PROJECT_DIRECTORY = f"{os.path.abspath('')}/../.."

if not os.environ.get("OPENAI_API_KEY", None):
    from dotenv import dotenv_values

    dotenv_values = dotenv_values(f"{PROJECT_DIRECTORY}/.env.dev")
    if "OPENAI_API_KEY" not in dotenv_values:
        raise ValueError(
            "OPENAI_API_KEY not found in either poetry environment nor in root .env.dev."
        )
    os.environ["OPENAI_API_KEY"] = dotenv_values["OPENAI_API_KEY"]

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3. Add a prompt template

prompt_optimizer.py
prompt_template = """
Figure out how badly I'm hallucinating in the following:
The 2020 Olympics Ping Pong Mens Singles Gold Medal was not won by Ma Long.
"""

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4. Upload Evaluation Dataset

Upload your own dataset for evaluating experiments.

Each dataset row should have:

  • Context
  • Instruction
  • Question
  • Answer
KeyRequiredDescriptionExample
ContextNoEnhances the prompt. Supplies additional background information to help the model generate accurate responses.“You are a doctor writing a visit note from a transcript of the doctor-patient conversation.”
InstructionNoEnhances the prompt. Provides specific guidance to the model on what action to perform.“Absolutely do not hallucinate. Capture only factual information.”
QuestionYesThe user input or query that prompts the model to generate a response. This is the only required key.“What were the main topics discussed?”
AnswerNo, required only if using a supervised evaluator such as cosine similarityThe expected output or response from the model, which serves as the benchmark for evaluation.“Investment strategies, retirement planning, and risk management.”
sample_evaluation_dataset = [
    {
        "Context": "A Hallucination Problem",
        "Instruction": "",
        "Question": "",
        "Answer": ""
    }
]

Example Dataset:

prompt_optimizer.py
  sample_evaluation_dataset = [
    {
        "Context": "A Hallucination Problem",
        "Instruction": "",
        "Question": "",
        "Answer": ""
    }
]

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5. Define Prompt Techniques

Nomadic enables you to create a diverse set of prompts from a target prompt.

You can experiment with the following prompt parameters:

KeyDescriptionRequiredSupported Parameters
prompt_tuning_approachChoose from prompting techniques.falsezero-shot - The model attempts to answer without specific examples, few-shot - Provides a few examples to guide the model’s response, chain-of-thought -Encourages the model to show its reasoning process
prompt_tuning_complexityAdjust the level of detail in the prompt.falsesimple - Creates straightforward, direct prompts, complex - Generates more detailed, nuanced prompts
prompt_tuning_focusFocus on different tasks.falseCommon options (customizable): fact extraction, action points, summary, simplify language, change tone, British English usage, template: [template_format]

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6. Define your evaluation function

Choose one of our off-the shelf evaluation metrics, or define a custom evaluator.

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Standard Evaluator

See Evaluations.

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Custom Evaluator

Define a custom metric using the Nomadic custom_evaluate method.

Sample Custom Evaluator:

prompt_optimizer.py
evaluator={
        # The "custom_evaluate" method allows for tailored evaluation metrics.
        "method": "custom_evaluate",
        # Evaluation metrics:
        # These criteria will be used to score each model output.
        # Each metric is typically scored on a scale (e.g., 0-20).
        "evaluation_metrics": [
            {"metric": "Accuracy", "weight": 0.75},
            # 2. "Completeness of information": Does the summary cover all important points from the meeting?
            {"metric": "Easiness to Understand", "weight": 0.1},
            # 3. "Clarity and coherence": Is the summary well-structured and easy to understand?
            {"metric": "Clarity and coherence", "weight": 0.1},
            # 4. "Actionable advice": Does the summary provide clear, actionable financial recommendations?
            {"metric": "Actionable advice", "weight": 0.025},
            # 5. "Professional tone": Is the language appropriate for a professional financial context?
            {"metric": "Professionalism", "weight": 0.025}
        ]
        # Consider adding or modifying these metrics based on your specific needs.
        # The evaluation results will help identify which parameter combinations produce the best summaries.
    }

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7. Create your Experiment using the Nomadic library

Experiment with your prompts by leveraging the same Nomadic experimentation library as generic experiments.

experiment = Experiment(
    params={
        "temperature", "max_tokens",
        # User Guidance: Prompt Tuning Parameters
        "prompt_tuning_approach", "prompt_tuning_complexity", "prompt_tuning_focus"
    },
    evaluation_dataset=sample_evaluation_dataset,
    user_prompt_request=prompt_template,
    model= OpenAIModel(model="gpt-4o-mini", api_keys={"OPENAI_API_KEY": os.environ["OPENAI_API_KEY"]}),
    # User Guidance: Evaluator Configuration
    # This section defines how the model's outputs will be assessed.
    evaluator=evaluator,
    search_method="grid",
    enable_logging=True,
    use_flaml_library=False,
)

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8. Run your Experiment!

prompt_optimizer.py
experiment_result = experiment.run(
    param_dict={
        "temperature": tune.choice([0.1, 0.9]),
        "max_tokens": tune.choice([250, 500]),
        "prompt_tuning_approach": tune.choice(["zero-shot", "few-shot", "chain-of-thought"]),
        "prompt_tuning_complexity": tune.choice(["simple", "complex"]),
        "prompt_tuning_focus": tune.choice(["fact extraction", "action points"])
    }
)

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9. Interpret Results

To identify the best-performing prompt technique, run

experiment_result.run_results[0].metadata

The experiment.visualize_results() function provides a comprehensive visual analysis & statistical summary of the experiment results, focusing on the score distribution and its relationship with various hyperparameters.

experiment.visualize_results()

To test signifiance, test_significance runs a Mann-Whitney U test to compare the top-performing parameter combinations against the rest of the results.

experiment.test_significance(n=5)  # Compares top 5 results against the rest
Optimizing ParametersContinuous Optimization