Complete AI Training

Prompt · Project Managers

Build Regulatory Update Chatbot

Use this when you want a conversational interface to deliver real-time regulatory updates and answer compliance questions.

All 17 prompts in this lesson

How to use it

  1. Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
  2. Replace every {{placeholder}} with your own details, or let the AI ask you for them.
  3. Use the follow-ups below to go deeper.
Prompt

Role You are a chatbot designer and compliance expert who builds conversational assistants that keep project managers informed and compliant.

Context you provide

  • {{industry}} — the sector for which the chatbot will track regulations.
  • {{regulatory_sources}} — the official sources or feeds the chatbot should monitor.
  • {{user_questions}} — the types of compliance questions the chatbot should answer.
  • {{delivery_channels}} — where the chatbot will live (e.g., Slack, Teams, web).

Instructions

  1. Ask for any missing inputs before starting.
  2. Design a chatbot architecture that ingests regulatory updates from the specified sources and stores them in a searchable format.
  3. Define the chatbot's conversational flow: greeting, update summaries, and Q&A on specific regulations.
  4. Specify how the chatbot will deliver real-time notifications (e.g., push messages, daily digests) and on which channels.
  5. Outline the knowledge base structure and how the chatbot will retrieve accurate answers.

Output format A chatbot specification with sections: Purpose, Data Sources, Conversation Flow, Notification Logic, and Technical Requirements. Use bullet points and clear headings.

Guardrails

  • Do not claim the chatbot can provide legal advice; frame it as informational.
  • Flag any assumptions about the user's technical stack.
  • Ensure the design prioritizes data accuracy and source citation.

Example Industry: healthcare; sources: CMS, FDA; user questions: 'What changed in HIPAA?'; delivery: Slack.

Follow-up prompts

  • How can I test the chatbot's accuracy before deployment?
  • What are the best practices for handling ambiguous user queries?
  • Can you suggest a fallback for when the chatbot cannot find an answer?