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Model Testing – Interactive Sessions

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    Playground
    Playground
    Updated on 23 Sep 2025
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    Playground is space where users can interact with AI models in a chat-like format. It’s designed for testing messages, evaluating model responses, and adjusting model behavior.

    Alt text

    You can follow guide to evaluate your model:

    Step 1: Adjust parameters

    Parameters include:

    Name Description Type Supported value
    Temperature Controls randomness in assistant responses. Lower = more focused, higher = more creative. Float [0.00, 2.00] (commonly 1.00 is balanced)
    Add stop sequence Defines where the model should stop generating text. String Custom string(s)
    Output length Limits the number of tokens in the response. Int [0, 8192]
    Top-P Controls diversity via nucleus sampling. Lower = more focused Float (0.00, 1.00]

    We recommend you adjust parameters based on each purpose:

    Purpose Temperature Add stop sequence Output length Top-P
    Creative writing 1.0–1.5 500+ 0.8–1.0
    Technical explanation 0.2–0.5 200–500 0.3–0.6
    Summarization 0.2–0.5 150–300 0.3–0.6
    Code generation 0.1–0.4 “n” or “#” 0.3–0.6
    Conversational agent 0.7–1.0 200–600 0.8–1.0

    Step 2: Start chatting

    The Playground interface is structured around Completions, which include:

    Prompts Description
    System messages Define the assistant’s behavior and tone. It helps guide how the model responds throughout the conversation.

    Eg: You are a creative writing assistant. Always write with vivid imagery, emotional depth, and a storytelling tone.
    User messages Represent the input or query from you. You can type text to test LLM models or add to upload an image to test VLM models.
    Assistant responses The AI model’s reply is based on user messages and system messages.

    You can interact with the model in real time, observe how it responds, and iterate based on your goals.

    Step 3: Refine your prompts

    To improve or explore different behaviors:

    • Modify the system message to change the assistant’s personality or tone.

    • Adjust the user message to test different types of queries.

    • Tweak the parameters to observe how the model’s output changes.

    Step 4: Clear completion

    1. Click icon Clear to reset and start a new completion.

    2. After clearing completion, completion is finished and stored in completion history.

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