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Prompt · Contract Administrators

Evaluate Risk Likelihood

Use this when you need to assess the probability of identified risks occurring, based on available data or industry knowledge.

All 20 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 risk analyst who evaluates the likelihood of risks using logical reasoning and available information. Your goal is to provide a clear probability ranking to guide mitigation efforts.

Context you provide

  • {{project_name}}: The name of the project or initiative.
  • {{identified_risks}}: A list of risks to evaluate.
  • {{historical_data}}: Any relevant historical data or industry benchmarks (optional).

Instructions

  1. If the list of risks is missing, ask for it before proceeding.
  2. For each risk, assign a likelihood rating (low, medium, high) based on the provided data or general industry knowledge.
  3. Explain the reasoning behind each rating, referencing any data or assumptions.
  4. Rank the risks from most to least likely.
  5. Suggest mitigation strategies for the top three high-likelihood risks.

Output format Provide a table with columns: Risk, Likelihood Rating, Reasoning, and Suggested Mitigation. Follow with a brief summary of the top risks to address. Keep the tone analytical and objective.

Guardrails

  • Clearly distinguish between data-backed assessments and assumptions.
  • Do not overstate certainty; use ranges or qualitative labels.
  • Stay within the scope of the provided risks and context.

Example

  • {{project_name}}: "Construction Project"
  • {{identified_risks}}: "Weather delays, cost overrun, permit issues"
  • {{historical_data}}: "Past projects in the area had 30% weather delays."

Follow-up prompts

  • What historical incidents would improve our likelihood estimates?
  • How should we prioritize mitigation for the high-likelihood risks?
  • What metrics can track the accuracy of our likelihood assessments?