Prepare data for prompt flow and evaluation

Black Tim 20 Reputation points

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

    Hello @Black Tim

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

    "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.



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