Skip to main content
Prompts HCCVN-AI-VN Pro Max System Optimizer

model software_engineering user risk: medium

HCCVN-AI-VN Pro Max System Optimizer

Act as a Leading AI Architect to optimize the HCCVN-AI-VN Pro Max public administration platform by developing a hybrid architecture with Agentic AI, Multimodal processing, and Fed…

  • Policy sensitive
  • Human review

PROMPT 

Act as a Leading AI Architect. You are tasked with optimizing the HCCVN-AI-VN Pro Max system — an intelligent public administration platform designed for Vietnam. Your goal is to achieve maximum efficiency, security, and learning capabilities using cutting-edge technologies.

Your task is to:
- Develop a hybrid architecture incorporating Agentic AI, Multimodal processing, and Federated Learning.
- Implement RLHF and RAG for real-time law compliance and decision-making.
- Ensure zero-trust security with blockchain audit trails and data encryption.
- Facilitate continuous learning and self-healing capabilities in the system.
- Integrate multimodal support for text, images, PDFs, and audio.

Rules:
- Reduce processing time to 1-2 seconds per record.
- Achieve ≥ 97% accuracy after 6 months of continuous learning.
- Maintain a self-explainable AI framework to clarify decisions.

Leverage technologies like TensorFlow Federated, LangChain, and Neo4j to build a robust and scalable system. Ensure compliance with government regulations and provide documentation for deployment and system maintenance.

OPTIONAL CONTEXT

  • government regulations

ROLES & RULES

Role assignments

  • Act as a Leading AI Architect.
  1. Reduce processing time to 1-2 seconds per record.
  2. Achieve ≥ 97% accuracy after 6 months of continuous learning.
  3. Maintain a self-explainable AI framework to clarify decisions.

EXPECTED OUTPUT

Format
structured_report
Constraints
  • include documentation for deployment and system maintenance

SUCCESS CRITERIA

  • Develop a hybrid architecture incorporating Agentic AI, Multimodal processing, and Federated Learning.
  • Implement RLHF and RAG for real-time law compliance and decision-making.
  • Ensure zero-trust security with blockchain audit trails and data encryption.
  • Facilitate continuous learning and self-healing capabilities in the system.
  • Integrate multimodal support for text, images, PDFs, and audio.
  • Leverage technologies like TensorFlow Federated, LangChain, and Neo4j.
  • Ensure compliance with government regulations.
  • Provide documentation for deployment and system maintenance.

FAILURE MODES

  • May propose unrealistic architectures unable to meet 1-2 second processing or 97% accuracy goals.
  • Might overlook integration of specific technologies like TensorFlow Federated or Neo4j.
  • Could inadequately address multimodal audio processing.
  • Risk of insufficient detail on Vietnam-specific government regulations.

CAVEATS

Missing context
  • Current system architecture or specifications.
  • Details on data sources and volumes.
  • Deployment environment (e.g., cloud provider, hardware).
  • Desired output format (e.g., diagrams, code, full documentation structure).
Ambiguities
  • Unclear if optimizing an existing system or designing from scratch.
  • 'Record' in processing time not defined.
  • Accuracy metric and dataset not specified.
  • Specific government regulations not listed.

QUALITY

OVERALL
0.70
CLARITY
0.85
SPECIFICITY
0.80
REUSABILITY
0.25
COMPLETENESS
0.75

IMPROVEMENT SUGGESTIONS

  • Replace specific system name 'HCCVN-AI-VN Pro Max' with placeholder like '{system_name}' for reusability.
  • Add 'Output a detailed architecture diagram in Mermaid syntax, followed by component descriptions, implementation roadmap, and deployment docs.'
  • Define key terms: e.g., 'A record is a single administrative document or citizen query.'
  • Include success criteria examples: 'Accuracy measured on benchmark dataset of Vietnamese legal cases.'

USAGE

Copy the prompt above and paste it into your AI of choice — Claude, ChatGPT, Gemini, or anywhere else you're working. Replace any placeholder sections with your own context, then ask for the output.

MORE FOR MODEL