Use parameters in prompts

Before running a prompt, you need to select which LLM processes it and configure how that model behaves. The Editor’s model settings panel gives you complete control over provider selection, generation parameters, and response formatting.

Select an LLM

Adaline’s Editor displays all supported LLMs based on the providers you have configured in your workspace settings. Open the model selector to browse and choose your preferred model:

Each model is displayed in the format provider::model_name. This prefix helps you distinguish between provider accounts when you have multiple keys configured for the same provider — for example, OpenAI-dev::gpt-4o and OpenAI-prod::gpt-4o.

To add a new AI provider, navigate to your workspace settings. See Configure AI Provider for setup instructions.

Configure generation settings

Click the settings icon next to the model selector to open the configuration panel:

Changing LLM settings in Adaline's Editor

The available parameters depend on the selected model. Common settings include:

Parameter	Description	Typical range
Temperature	Controls randomness in responses. Lower values produce more deterministic outputs; higher values increase creativity.	0 – 2
Max Tokens	Sets the maximum number of tokens the model can generate in a single response.	Model-dependent
Top P	Controls diversity via nucleus sampling. The model considers tokens whose cumulative probability reaches this threshold.	0 – 1
Frequency Penalty	Reduces repetition by penalizing tokens that have already appeared frequently.	-2 – 2
Presence Penalty	Encourages the model to introduce new topics by penalizing tokens that have appeared at all.	-2 – 2

The interface automatically shows only the parameters that are relevant to the model you have selected. Different providers and models support different subsets of these settings.

Configure response format

You can control the structure of the model’s output by configuring the response format. Click on Response format in the settings panel:

Choose from the following format options:

Format	Output	When to use
text	Free-form text (default)	General-purpose prompts — summarization, Q&A, creative writing, chat.
json_object	Valid JSON in an auto-determined schema	Quick prototyping when you need structured output but don’t need a fixed schema. Your prompt must contain the word “json” (case insensitive).
json_schema	JSON adhering to a strict schema you define	Production workflows — API integrations, structured data extraction, pipelines that depend on a consistent response shape.

Define a JSON schema

When using json_schema, provide a schema definition in the response format editor. Here is an example:

{
  "strict": true,
  "name": "users_data",
  "description": "A schema for creating user data.",
  "schema": {
    "type": "object",
    "properties": {
      "user_city": {
        "type": "string",
        "description": "The city where the user resides."
      },
      "user_name": {
        "type": "string",
        "description": "The full name of the user."
      },
      "user_age": {
        "type": "number",
        "description": "The age of the user in years."
      }
    },
    "required": ["user_name", "user_age", "user_city"],
    "additionalProperties": false
  }
}

Key requirements for the schema:

The "strict": true field is mandatory.
The name field is mandatory and must use underscores (e.g., users_data) or camelCase (e.g., usersData) — no spaces or special characters.
Define the structure inside the "schema" field using standard JSON Schema syntax.

The schema you provide must adhere to the OpenAI JSON schema structure. This format is used across all supported providers.

The image below shows a prompt configured with a JSON schema and the structured response from the model:

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Use parameters in prompts

Select an LLM

Configure generation settings

Configure response format

Define a JSON schema

Next steps

Use Roles in Prompts

Use Tools in Prompts

​Select an LLM

​Configure generation settings

​Configure response format

​Define a JSON schema

​Next steps

Use Roles in Prompts

Use Tools in Prompts

Select an LLM

Configure generation settings

Configure response format

Define a JSON schema

Next steps