Prepare data for prompt flow and evaluation

Black Tim 20 Reputation points
2024-06-30T21:57:56.0466667+00:00

Prompt Flow seems new. Please provide me some guidance for this topic

Azure Machine Learning
Azure Machine Learning
An Azure machine learning service for building and deploying models.
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  1. YutongTie-MSFT 48,326 Reputation points
    2024-06-30T23:02:00.95+00:00

    Hello @Black Tim

    Thanks for reaching out to us, have you went through the document for Prompt Flow ?

    https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/overview-what-is-prompt-flow?view=azureml-api-2

    "Prompt Flow" refers to a feature in Azure Machine Learning that facilitates the creation and management of conversational AI models, particularly those based on large language models like GPT (Generative Pre-trained Transformer). This feature enables you to build interactive dialogue systems where users can engage in a conversational manner with the AI model.

    Here’s how you can prepare data for prompt flow and effectively evaluate its performance within Azure Machine Learning:

    1. Understand Prompt Flow Basics

    Prompt Flow in Azure Machine Learning typically revolves around fine-tuning or adapting a large language model (such as GPT) to understand and respond appropriately to prompts or user inputs in a conversational context. This involves:

    Model Fine-tuning: Adjusting the model parameters, prompts, and examples to fit specific use cases or domains.

    Data Preparation: Curating datasets that include prompt-response pairs or dialogues that the model can learn from.

    1. Data Preparation for Prompt Flow

    a. Data Collection:

    Prompt-Response Pairs: Gather data where each example consists of a prompt (user input or query) and a corresponding response (desired output or answer).

    Dialogue Contexts: Include contextual information that might influence the response, such as previous interactions or specific scenarios.

    b. Data Formatting:

    Text Preprocessing: Clean and preprocess text data to remove noise, normalize text, handle special characters, and ensure consistency.

    Tokenization: Tokenize text into smaller units (words, subwords, or characters) suitable for the language model.

    Formatting for Training: Format data into a structure suitable for training the language model, typically as input-output pairs.

    1. Dataset Characteristics

    Size and Diversity: Ensure the dataset covers a diverse range of prompts and responses to generalize well across different user inputs.

    Quality: Verify the quality of prompts and responses to avoid bias, inaccuracies, or inappropriate content.

    1. Model Training and Evaluation

    a. Training Setup:

    Model Selection: Choose a suitable pre-trained language model (like GPT-3) as the base and fine-tune it using your curated dataset.

    Fine-tuning Parameters: Adjust hyperparameters such as learning rate, batch size, and number of epochs based on your dataset size and complexity.

    b. Evaluation:

    Metrics: Define evaluation metrics such as accuracy, fluency, relevance of responses, and coherence in dialogues.

    Validation Set: Split your dataset into training and validation sets to evaluate the model’s performance during training.

    1. Integration with Azure Machine Learning

    Workspace Setup: Ensure your Azure Machine Learning workspace is configured with necessary compute resources and dependencies.

    Experiment Tracking: Use Azure Machine Learning to track experiments, monitor model performance, and manage versions of your trained models.

    Example Scenario:

    Suppose you want to build a customer support chatbot using Azure Machine Learning's prompt flow:

    • Data Collection: Gather historical customer support chat logs, where each log includes user queries and corresponding support responses.
    • Data Preparation: Clean and preprocess chat logs, format them into prompt-response pairs, and tokenize them for model training.
    • Model Training: Fine-tune a pre-trained language model on the curated dataset, adjusting prompts and responses to handle customer queries effectively.
    • Evaluation: Evaluate the chatbot’s performance using validation data, assessing response relevance, coherence, and overall user satisfaction.

    I hope this helps! Let us know if you have any question regarding to this process.

    Regards,

    Yutong

    -Please kindly accept the answer if you feel helpful to support the community, thanks a lot.

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