> ## Documentation Index > Fetch the complete documentation index at: https://docs.narrative.io/llms.txt > Use this file to discover all available pages before exploring further. # Prompt Studio Interface > Reference for the Prompt Studio UI for creating fine-tuning-ready datasets This reference documents the UI elements, configuration options, and actions available in the Prompt Studio interface. ## Overview Prompt Studio transforms datasets into structured, fine-tuning-ready examples for AI models. Each row in the output dataset represents a valid training example formatted as a conversation with system, user, and assistant messages. **Path:** My Models → Prompt Studio *** ## Prompt Builder module The Prompt Builder is the main workspace for configuring how dataset rows transform into conversation-formatted training examples. ### Dataset selector | Element | Description | | -------------------- | ------------------------------------------------ | | **Dataset dropdown** | Select the source dataset to transform | | **Row count** | Displays total rows available for transformation | The selected dataset provides the fields available for macro substitution in prompts. ### Role configuration panels Three panels allow you to configure prompts for each conversation role: | Role | Purpose | Typical content | | ------------- | ---------------------------------- | --------------------------------------------- | | **System** | Sets the AI's behavior and context | Instructions, persona definition, constraints | | **User** | Represents the human input | Questions, requests, data to analyze | | **Assistant** | Represents the AI response | Expected output format, example responses | Each panel contains: | Element | Description | | -------------------- | ------------------------------------------------------ | | **Select button** | Opens the prompt editor for that role | | **Prompt text area** | Displays the configured prompt with macro placeholders | | **Macro indicators** | Visual badges showing embedded macros | *** ## Prompt editor The prompt editor opens when you click **Select** on any role panel. ### Text input area | Element | Description | | ---------------------------- | --------------------------------------------- | | **Prompt text field** | Multi-line text area for writing the prompt | | **Macro placeholder syntax** | Use `{{macro_name}}` to insert dynamic values | | **Character count** | Displays current prompt length | ### Macro configuration After adding macro placeholders to your prompt text, configure each macro's data source: | Macro type | Description | Use case | | ----------- | ----------------------------------------------------------------- | ------------------------------------------------------ | | **Field** | Maps to a column in the source dataset | Pull values directly from dataset columns | | **NQL** | Uses a [Narrative Query Language](/nql/general/syntax) expression | Compute derived values, transform data, combine fields | | **Literal** | Fixed text value | Static content that doesn't change per row | ### Macro configuration panel | Element | Description | | ----------------------- | ----------------------------------------------------- | | **Macro name** | The identifier used in `{{macro_name}}` syntax | | **Type selector** | Choose between Field, NQL, or Literal | | **Value configuration** | Field picker, NQL editor, or text input based on type | | **Preview value** | Sample resolved value from the first dataset row | ### Editor actions | Action | Description | | ---------- | ------------------------------------------------------------ | | **Save** | Saves the prompt configuration and returns to Prompt Builder | | **Cancel** | Discards changes and returns to Prompt Builder | *** ## Preview screen The Preview screen validates your prompt configuration by showing resolved output for each dataset row. **Path:** Click **Preview** in the Prompt Builder ### Conversation display | Element | Description | | --------------------- | ----------------------------------------------------- | | **System message** | Resolved system prompt with all macros substituted | | **User message** | Resolved user prompt with all macros substituted | | **Assistant message** | Resolved assistant prompt with all macros substituted | Messages display in a conversational format, visually distinguishing each role. ### Navigation controls | Control | Description | | ----------------- | ----------------------------------------------------------- | | **Prev** | Navigate to the previous dataset row | | **Next** | Navigate to the next dataset row | | **Row indicator** | Shows current row number and total (e.g., "Row 5 of 1,234") | ### Validation indicators | Indicator | Meaning | | ------------------- | ------------------------------------------------------ | | **Green checkmark** | All macros resolved successfully | | **Yellow warning** | Some macros returned empty or null values | | **Red error** | Macro resolution failed (e.g., invalid NQL expression) | *** ## Output format The transformed dataset contains rows formatted as structured conversations: ```json theme={null} { "messages": [ { "role": "system", "content": "You are a helpful assistant..." }, { "role": "user", "content": "Analyze this customer data..." }, { "role": "assistant", "content": "Based on the data provided..." } ] } ``` This format is compatible with fine-tuning APIs from major AI model providers. *** ## Actions reference ### Builder actions | Action | Location | Description | Result | | ------------------ | ---------------------- | ----------------------------- | ------------------------------------------ | | **Select dataset** | Dataset module | Choose source dataset | Dataset fields become available for macros | | **Configure role** | Role panel | Open prompt editor for a role | Prompt editor opens | | **Preview** | Prompt Builder toolbar | Validate prompt resolution | Preview screen opens | ### Editor actions | Action | Location | Description | Result | | ------------------- | -------------- | ------------------------- | --------------------------- | | **Add macro** | Prompt text | Type `{{name}}` syntax | Macro placeholder created | | **Configure macro** | Macro panel | Set type and value | Macro resolution configured | | **Save** | Editor toolbar | Save prompt configuration | Returns to Prompt Builder | ### Preview actions | Action | Location | Description | Result | | --------------------- | ------------------- | --------------------------- | -------------------------------------- | | **Navigate rows** | Navigation controls | View different dataset rows | Conversation updates with new row data | | **Return to builder** | Preview toolbar | Exit preview mode | Returns to Prompt Builder | ### Output actions | Action | Location | Description | Result | | -------------------- | ---------------------- | --------------------------- | ------------------------------------------- | | **Generate dataset** | Prompt Builder toolbar | Create materialized dataset | Opens Data Studio with prompt configuration | *** ## Workflow summary 1. **Select dataset** → Choose source data in the Dataset module 2. **Configure prompts** → Define system, user, and assistant prompts with macros 3. **Configure macros** → Map each macro to a field, NQL expression, or literal value 4. **Preview output** → Validate resolved prompts row by row 5. **Generate dataset** → Use Data Studio to materialize the transformed dataset *** ## Related content Train and fine-tune models using prepared datasets Reference for NQL expressions used in macros Using AI models within your data plane Generate datasets from your prompt configurations