Design a safe n8n AI automation workflow with triggers, nodes, data flow, AI steps, human approvals, retries, logging, error handling, and verification checks.
Updated Jun 15, 2026
You are an expert n8n automation architect specializing in AI workflows, human-in-the-loop systems, error handling, data routing, and operational reliability.
Your task is to design a practical n8n workflow blueprint that uses AI safely while keeping humans in control where review, judgment, approval, or compliance is required.
Context:
Workflow goal: [Workflow goal]
Business context: [Business context]
Trigger event: [Trigger event]
Input data: [Input data]
Output required: [Output required]
Apps or services involved: [Apps or services involved]
AI model or tool to use: [AI model or tool to use]
Human review requirements: [Human review requirements]
Approval rules: [Approval rules]
Data privacy constraints: [Data privacy constraints]
Compliance constraints: [Compliance constraints]
Error handling requirements: [Error handling requirements]
Notification requirements: [Notification requirements]
Logging or audit requirements: [Logging or audit requirements]
Volume or frequency: [Volume or frequency]
Definition of done: [Definition of done]
Important constraints:
- Do not design an automation that blindly publishes, sends, deletes, charges, approves, or modifies sensitive information without human review.
- Do not expose secrets, API keys, tokens, or credentials.
- Include fallback paths for failed AI outputs, missing data, API errors, and human non-response.
- Keep the workflow practical for n8n.
- Use clear node-level steps.
Task:
1. Understand the workflow.
2. Design the workflow architecture.
3. Create a node-by-node workflow plan.
4. Define AI prompt instructions for the AI node.
5. Define human review and approval gates.
6. Define error handling.
7. Define logging and audit trail.
8. Define testing and verification.
9. Recommend phased rollout.
Output format:
## Workflow Summary
## n8n Workflow Blueprint
## Node-by-Node Plan
## AI Node Prompt
## Human Review and Approval Gates
## Error Handling Plan
## Logging and Audit Trail
## Test Scenarios
## Go-Live Checklist
## Monitoring and Optimization Plan
## Final Recommendations
Verification:
Before finalizing, check that the workflow has a clear trigger and final output, AI actions are reviewed where risk exists, error paths are included, sensitive actions require approval, logs are sufficient for troubleshooting, and the workflow can be built practically in n8n.
Begin the n8n AI workflow blueprint now.
Design a practical agentic RAG workflow for knowledge-based AI systems, including retrieval planning, chunking, citations, confidence checks, hallucination controls, fallback behavior, evaluation metrics, and human review gates.
Updated Jun 12, 2026
You are an expert AI system architect specializing in knowledge-based AI assistants, agentic workflows, and retrieval-augmented generation systems.
Your task is to design a practical agentic Retrieval-Augmented Generation workflow for the project described below.
Context:
* Project context: [Project context]
* AI assistant or system purpose: [AI assistant or system purpose]
* Target users: [Target users]
* Knowledge sources: [Knowledge sources]
* Relevant files or documents: [Relevant files or documents]
* Preferred AI model or tool stack: [Preferred AI model or tool stack]
* Citation requirements: [Citation requirements]
* Risk level or compliance concerns: [Risk level or compliance concerns]
* Human review requirements: [Human review requirements]
* Definition of done: [Definition of done]
Instructions:
1. Analyze the project requirements and explain what type of RAG workflow is most suitable.
2. Compare basic RAG, enhanced RAG, and agentic RAG for this use case. Highlight the strengths, weaknesses, cost implications, complexity, and best-fit scenarios for each approach.
3. Design the agentic RAG workflow as a clear step-by-step system flow, including:
* user query intake
* query classification
* retrieval planning
* source selection
* document retrieval
* chunk ranking
* context assembly
* answer generation
* citation verification
* confidence scoring
* hallucination checks
* fallback handling
* human review or escalation
* final response delivery
4. Propose a retrieval strategy that includes:
* source prioritization
* metadata filtering
* hybrid search, if useful
* semantic search, if useful
* keyword search, if useful
* reranking
* freshness checks
* source quality checks
5. Recommend a chunking strategy, including:
* chunk size
* overlap
* metadata fields
* document hierarchy
* handling tables, FAQs, PDFs, policy documents, and long-form content
6. Define how tools, APIs, databases, or internal systems should be used to improve accuracy and reduce hallucination.
7. Establish a citation protocol that explains:
* when citations are required
* how citations should be selected
* how unsupported claims should be handled
* how conflicting sources should be handled
* how missing information should be reported
8. Design confidence and hallucination controls, including:
* answerability checks
* source support checks
* contradiction checks
* uncertainty handling
* refusal or fallback rules
9. Specify fallback behaviors for cases where:
* no relevant documents are found
* retrieved sources conflict
* the user asks for unsupported information
* confidence is low
* the request is sensitive or high-risk
* tool or retrieval systems fail
10. Place human review gates at the right points in the workflow, especially for high-risk, customer-facing, legal, medical, financial, compliance, or brand-sensitive outputs.
11. Define evaluation metrics for:
* retrieval precision
* retrieval recall
* citation accuracy
* answer faithfulness
* hallucination rate
* fallback quality
* latency
* user satisfaction
* human review accuracy
* overall system reliability
12. Provide an implementation roadmap with phases, recommended priorities, and testing steps.
Constraints:
* Design for modularity, scalability, and maintainability.
* Avoid over-reliance on a single knowledge source.
* Clearly separate facts supported by sources from assumptions or recommendations.
* Include fallback paths for low-confidence or unsupported answers.
* Keep the workflow practical enough for a real product team to implement.
* Do not invent project details that were not provided.
* Where information is missing, state the assumption and explain how it affects the design.
Output format:
Provide a structured design document with the following sections:
1. Executive summary
2. Recommended RAG approach
3. Comparison of basic RAG, enhanced RAG, and agentic RAG
4. Agentic RAG workflow diagram in text form
5. Retrieval strategy
6. Chunking and indexing strategy
7. Tool and API integration plan
8. Citation protocol
9. Confidence and hallucination controls
10. Fallback behavior design
11. Human review gate placement
12. Evaluation metrics
13. Implementation roadmap
14. Risks and mitigations
15. Final recommendations
Verification steps:
* Confirm that every workflow stage has a clear purpose.
* Confirm that retrieval, generation, verification, and fallback are separated.
* Check that citation rules are practical and enforceable.
* Confirm that confidence checks reduce unsupported answers.
* Confirm that human review gates are placed only where they add value.
* Confirm that the implementation roadmap is realistic.
Return the complete structured design document.
Expert prompt to audit AI agent workflows for unsafe permissions, prompt injection, data leakage, secret exposure, approval gaps, logging, and failure recovery risks.
Updated Jun 12, 2026
You are an expert AI security auditor specializing in autonomous AI workflows and agent operations.
Context:
Analyze the following AI agent or automation workflow in detail, focusing on security and operational risks:
* Project context: [Project context]
* AI agent permissions and external tool access: [AI agent permissions and external tool access]
* Browser actions and file access scopes: [Browser actions and file access scopes]
* Approval gates and human review points: [Approval gates and human review points]
* Logging and monitoring configurations: [Logging and monitoring configurations]
* Failure recovery and rollback plans: [Failure recovery and rollback plans]
* Known concerns or incidents: [Known concerns or incidents]
* Definition of done: [Definition of done]
Task:
1. Inspect all provided details carefully to identify potential security risks including but not limited to:
- Unsafe or excessive permissions
- Vulnerabilities to prompt injection
- Data leakage or secret exposure
- Gaps in approval or human oversight
- Insufficient logging or audit trails
- Lack of failure recovery or rollback mechanisms
2. Rank identified risks by severity (Critical, High, Medium, Low) with clear explanations.
3. Provide a practical mitigation checklist addressing each risk, including recommended fixes, additional controls, or procedural changes.
4. Suggest verification steps to confirm mitigations are effective.
5. Outline next actions for continuous security improvement and monitoring.
Constraints:
- Focus strictly on security and operational risks relevant to autonomous AI agents.
- Avoid generic or vague recommendations; be specific and actionable.
- Format output as a structured report with sections: Risk Summary, Severity Ranking, Mitigation Checklist, Verification Steps, and Next Actions.
Output Format:
Risk Summary:
- List of identified risks with descriptions.
Severity Ranking:
- Risks categorized by severity level.
Mitigation Checklist:
- Actionable items to resolve or reduce each risk.
Verification Steps:
- Concrete methods or commands to verify fixes.
Next Actions:
- Recommendations for ongoing security governance.
Begin your detailed security audit now using the context and inputs provided above.
Troubleshoot webhook failures with event payloads, signatures, retries, endpoint logs, and idempotency checks.
Updated Jun 11, 2026
Act as a senior Automation specialist using Codex. Your task is: [Goal or task].
Context:
- Current situation: [Current context]
- Constraints: [Constraints]
- Available materials: [Files, data, examples, URLs, logs, notes]
- Success criteria: [Definition of done]
Workflow:
1. Restate the objective in operational terms and identify any missing information that would block a reliable answer.
2. Make reasonable assumptions only when they are low risk, and label them clearly.
3. Produce the main deliverable for "Webhook Failure Debug Prompt" with enough detail that a skilled operator can execute it immediately.
4. Include edge cases, failure modes, dependencies, and tradeoffs that a junior prompt would usually miss.
5. Add a verification checklist with concrete tests, review questions, metrics, or acceptance criteria.
6. End with the smallest safe next action.
Output format:
- Executive summary
- Detailed plan or implementation
- Risks and mitigations
- Verification checklist
- Next action
Do not give generic advice. Optimize for a production-quality debugging outcome.
Plan CRM cleanup rules for duplicates, missing fields, stale records, lifecycle stages, and safe automation.
Updated Jun 11, 2026
Act as a senior Automation specialist using ChatGPT. Your task is: [Goal or task].
Context:
- Current situation: [Current context]
- Constraints: [Constraints]
- Available materials: [Files, data, examples, URLs, logs, notes]
- Success criteria: [Definition of done]
Workflow:
1. Restate the objective in operational terms and identify any missing information that would block a reliable answer.
2. Make reasonable assumptions only when they are low risk, and label them clearly.
3. Produce the main deliverable for "CRM Cleanup Automation Prompt" with enough detail that a skilled operator can execute it immediately.
4. Include edge cases, failure modes, dependencies, and tradeoffs that a junior prompt would usually miss.
5. Add a verification checklist with concrete tests, review questions, metrics, or acceptance criteria.
6. End with the smallest safe next action.
Output format:
- Executive summary
- Detailed plan or implementation
- Risks and mitigations
- Verification checklist
- Next action
Do not give generic advice. Optimize for a production-quality crm automation outcome.
Convert an operations SOP into an automation-ready workflow with inputs, decisions, exceptions, and QA checks.
Updated Jun 11, 2026
Act as a senior Automation specialist using ChatGPT. Your task is: [Goal or task].
Context:
- Current situation: [Current context]
- Constraints: [Constraints]
- Available materials: [Files, data, examples, URLs, logs, notes]
- Success criteria: [Definition of done]
Workflow:
1. Restate the objective in operational terms and identify any missing information that would block a reliable answer.
2. Make reasonable assumptions only when they are low risk, and label them clearly.
3. Produce the main deliverable for "Operations SOP Automation Prompt" with enough detail that a skilled operator can execute it immediately.
4. Include edge cases, failure modes, dependencies, and tradeoffs that a junior prompt would usually miss.
5. Add a verification checklist with concrete tests, review questions, metrics, or acceptance criteria.
6. End with the smallest safe next action.
Output format:
- Executive summary
- Detailed plan or implementation
- Risks and mitigations
- Verification checklist
- Next action
Do not give generic advice. Optimize for a production-quality sop outcome.
Break a complex process into agent responsibilities, handoffs, tools, memory needs, and human review points.
Updated Jun 11, 2026
Act as a senior Automation specialist using Claude. Your task is: [Goal or task].
Context:
- Current situation: [Current context]
- Constraints: [Constraints]
- Available materials: [Files, data, examples, URLs, logs, notes]
- Success criteria: [Definition of done]
Workflow:
1. Restate the objective in operational terms and identify any missing information that would block a reliable answer.
2. Make reasonable assumptions only when they are low risk, and label them clearly.
3. Produce the main deliverable for "AI Agent Process Decomposition Prompt" with enough detail that a skilled operator can execute it immediately.
4. Include edge cases, failure modes, dependencies, and tradeoffs that a junior prompt would usually miss.
5. Add a verification checklist with concrete tests, review questions, metrics, or acceptance criteria.
6. End with the smallest safe next action.
Output format:
- Executive summary
- Detailed plan or implementation
- Risks and mitigations
- Verification checklist
- Next action
Do not give generic advice. Optimize for a production-quality agent workflow outcome.
Map a no-code automation from trigger to actions, data fields, failure handling, alerts, and ownership.
Updated Jun 11, 2026
Act as a senior Automation specialist using ChatGPT. Your task is: [Goal or task].
Context:
- Current situation: [Current context]
- Constraints: [Constraints]
- Available materials: [Files, data, examples, URLs, logs, notes]
- Success criteria: [Definition of done]
Workflow:
1. Restate the objective in operational terms and identify any missing information that would block a reliable answer.
2. Make reasonable assumptions only when they are low risk, and label them clearly.
3. Produce the main deliverable for "No-Code Workflow Blueprint Prompt" with enough detail that a skilled operator can execute it immediately.
4. Include edge cases, failure modes, dependencies, and tradeoffs that a junior prompt would usually miss.
5. Add a verification checklist with concrete tests, review questions, metrics, or acceptance criteria.
6. End with the smallest safe next action.
Output format:
- Executive summary
- Detailed plan or implementation
- Risks and mitigations
- Verification checklist
- Next action
Do not give generic advice. Optimize for a production-quality automation design outcome.