A/B Split Testing

A/B Split Testing automates the process of comparing two different prompts for AI chat sessions, enhancing response quality by randomly assigning users to either a baseline or alternative prompt. This workflow integrates with LangChain and Supabase to track session data, ensuring consistent interactions within each session. By leveraging this split testing approach, users can optimize AI performance and gain insights into which prompts yield better engagement and results.

7/8/2025
16 nodes
Complex
manualcomplexlangchainsupabasesticky noteadvancedlogicconditionaldatabasedata
Categories:
Complex WorkflowManual Triggered
Integrations:
LangChainSupabaseSticky Note

Target Audience

  • Marketing Teams: Those looking to optimize their messaging strategies through A/B testing.
    - Product Managers: Professionals who want to understand user preferences for better product decisions.
    - Data Analysts: Individuals focused on analyzing the effectiveness of different prompts and their impact on user engagement.
    - Developers: Those who need to implement automated workflows integrating AI and databases for real-time feedback and adjustments.

    • Problem Solved

    This workflow addresses the challenge of effectively testing different prompts for an AI language model. By randomly assigning chat sessions to either a baseline or alternative prompt, it provides a systematic way to measure which messaging strategy yields better user engagement and satisfaction. This is crucial for optimizing AI interactions and enhancing user experience.

    Workflow Steps

  • Receive Chat Message: The workflow begins when a chat message is received via a webhook.
    2. Check Session Existence: It checks if the session ID already exists in the Supabase database to prevent duplicate entries.
    3. Session Handling:
    - If the session exists, it retrieves the assigned prompt.
    - If the session does not exist, it randomly assigns a prompt (baseline or alternative) and adds the session ID to the database.
    4. Define Prompt Values: The workflow defines the prompt values for both the baseline and alternative options.
    5. AI Agent Interaction: The chosen prompt is sent to the AI agent to generate a response.
    6. Memory Management: The chat history is stored in a PostgreSQL database to maintain context for future interactions.

    • Customization Guide

  • Modify Prompt Values: Users can adjust the values of the baseline and alternative prompts in the Define Path Values node to suit their specific needs.
    2. Change AI Model: Users can replace the AI model used in the OpenAI Chat Model node to experiment with different language models.
    3. Database Configuration: Ensure the Supabase database is set up with the required tables and fields as mentioned in the setup instructions.
    4. Testing Different Scenarios: Users are encouraged to test various chat sessions to observe how changes in prompts affect user engagement and satisfaction.
    5. Metrics Tracking: Implement additional tracking metrics to evaluate the effectiveness of the prompts beyond just user interaction.

    • A/B Split Testing - N8N Workflow Directory