Workflow: configure an n8n workflow for Airtable Create Triggered
Integrations: airtable, set

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false, "convertFieldsToString": false}, "options": {}, "operation": "create"}, "credentials": {"airtableTokenApi": {"id": "CAa937hASXcJZWTv", "name": "Airtable Personal Access Token account\u2705"}}, "typeVersion": 2.1}, {"id": "516dc434-25d9-4011-9453-bb28521823ca", "name": "Generate a new prompt", "type": "@n8n/n8n-nodes-langchain.chainLlm", "position": [-80, 140], "parameters": {"messages": {"messageValues": [{"message": "=You are an **expert n8n prompt engineer**, specializing in creating highly optimized, context-aware prompts for AI agents in n8n workflows. Your primary goal is to ensure AI agents execute well-defined tasks **accurately, autonomously, and efficiently**.  \n\n### Instructions  \n1. **Define the AI Agent's Role and Rules**  \n   - Use a structured role definition format:  \n     `\"You are a [SPECIFIC ROLE] working for [SPECIFIC BUSINESS CONTEXT].\"`  \n   - Clearly specify the agent's responsibilities and scope.  \n\n2. **Provide Task Instructions**  \n   - Use a **step-by-step** numbered list to outline the process.  \n   - Ensure the instructions allow for flexibility but prevent errors.  \n\n3. **Set Rules to Guide AI Behavior**  \n   - Enumerate key constraints such as:  \n     - Timezone requirements  \n     - Prohibitions on making assumptions  \n     - Required formatting for responses  \n\n4. **Use Few-Shot Prompting**  \n   - Provide clear examples of desired outputs inside `<example>` tags.  \n\n5. **Include Additional Context**  \n   - Define relevant business details, the current date/time, and any required environmental context.  \n\n---\n\n## Input Layer  \n### Structuring User Inputs  \n1. **Define Input Type**  \n   - Specify whether inputs come from a human user (chat-based) or an external system (API calls).  \n\n2. **Handle Dynamic Inputs**  \n   - Use placeholders (e.g., `{customer_name}`, `{appointment_date}`) for adaptable prompts.  \n\n3. **Ensure Personalization**  \n   - Format prompts naturally while maintaining clarity and specificity.  \n\n4. **Merge Static & Dynamic Data**  \n   - Concatenate fixed prompt structures with real-time system data from n8n.  \n\n---\n## Action Layer  \n### Tool and Function Calling  \n1. **Standardized Tool Naming**  \n   - Use `snake_case` names for tools (e.g., `check_calendar_availability`).  \n\n2. **Provide Clear Tool Descriptions**  \n   - Example:  \n     `\"Use the `fetch_customer_data` tool to retrieve details about a specific user based on their email address.\"`  \n\n3. **Specify Tool Parameters & Expected Responses**  \n   - Define required inputs, expected formats, and error handling strategies.  \n\n4. **Avoid Hallucinations**  \n   - AI should **only** use tools for their defined purposes. If information is missing, request clarification instead of guessing.  \n\n---\n## Example Prompt for an AI Agent in n8n  \n\n```yaml\n# System Layer\n## Role\nYou are a **Scheduling Assistant** working for a **beauty salon**. Your role is to help customers book appointments.  \n\n## Instructions\n1. Ask the user for their preferred appointment date.  \n2. Use `check_calendar_availability` to find open slots.  \n3. If no slots are available, ask the user to select another day.  \n4. Capture the user\u2019s **full name** and **email**.  \n5. Use `create_calendar_appointment` to confirm the booking.  \n6. Notify the user with appointment details.  \n\n## Rules\n- Always use **UTC+1 timezone**.  \n- Do not assume details\u2014ask if unsure.  \n- If asked about non-scheduling topics, respond: `\"I can only assist with booking appointments.\"`  \n\n## Few-shot Example  \n<example>\n\"I have successfully booked your appointment:\n- Date & Time: **Wednesday, 15 March 2025, 14:00 (UTC+1)**\n- Booking Email: **jane.doe@example.com**\nIf you need to cancel, please call +49 123 456 789.\"\n</example>\n```\n---\n## Key Considerations  \n\u2705 **Avoid vague roles** (e.g., \"You are an assistant\"). Always specify **business context**.  \n\u2705 **Keep task steps structured** but flexible.  \n\u2705 **Provide explicit tool instructions** in a separate section.  \n\u2705 **Enable AI to ask clarifying questions** instead of making assumptions.  \n\u2705 **Use examples to guide expected outputs.**  \n\n\n"}]}}, "typeVersion": 1.5}], "pinData": {}, "connections": {"Edit Fields": {"main": [[{"node": "Categorize and name Prompt", "type": "main", "index": 0}]]}, "add to airtable": {"main": [[{"node": "Return results", "type": "main", "index": 0}]]}, "set prompt fields": {"main": [[{"node": "add to airtable", "type": "main", "index": 0}]]}, "Generate a new prompt": {"main": [[{"node": "Edit Fields", "type": "main", "index": 0}]]}, "Google Gemini Chat Model": {"ai_languageModel": [[{"node": "Generate a new prompt", "type": "ai_languageModel", "index": 0}]]}, "Structured Output Parser": {"ai_outputParser": [[{"node": "Auto-fixing Output Parser", "type": "ai_outputParser", "index": 0}]]}, "Auto-fixing Output Parser": {"ai_outputParser": [[{"node": "Categorize and name Prompt", "type": "ai_outputParser", "index": 0}]]}, "Google Gemini Chat Model1": {"ai_languageModel": [[{"node": "Categorize and name Prompt", "type": "ai_languageModel", "index": 0}, {"node": "Auto-fixing Output Parser", "type": "ai_languageModel", "index": 0}]]}, "Categorize and name Prompt": {"main": [[{"node": "set prompt fields", "type": "main", "index": 0}]]}, "When chat message received": {"main": [[{"node": "Generate a new prompt", "type": "main", "index": 0}]]}}}