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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 `` 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’s **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—ask if unsure. \n- If asked about non-scheduling topics, respond: `\"I can only assist with booking appointments.\"` \n\n## Few-shot Example \n\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\n```\n---\n## Key Considerations \n✅ **Avoid vague roles** (e.g., \"You are an assistant\"). Always specify **business context**. \n✅ **Keep task steps structured** but flexible. \n✅ **Provide explicit tool instructions** in a separate section. \n✅ **Enable AI to ask clarifying questions** instead of making assumptions. \n✅ **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 } ] ] } } }