A "pre-mortem" is a powerful project management exercise where a team imagines that their project has failed, and then works backward to figure out what could have led to that failure. It's a fantastic way to uncover risks that normal brainstorming might miss. You can use an AI to facilitate this process, especially by grounding it in the realities of your past projects.

The Strategy: Combining Imagination with Historical Data

This prompt asks the AI to take on a creative, pessimistic persona and connect its imagined failures to concrete data you provide.

Step 1: Gather Data from a Past Failed or Challenged Project Find a post-mortem or lessons-learned document from a project that struggled. It doesn't have to be a complete failure—just one that had significant issues. Anonymize the document, removing names and sensitive details.

Example Anonymized Data:

"Post-mortem for [Project Zebra]:

• Final delivery was 6 weeks late.
• Root Cause Analysis: The chosen third-party API for data processing was unreliable, causing frequent outages. The team spent 3 weeks building workarounds.
• Key Takeaway: The technical feasibility of critical third-party dependencies was not sufficiently vetted during the planning phase."

Step 2: Craft Your "Pre-Mortem" Prompt Now, write a prompt that sets the scene for failure and asks the AI to connect it to your past experiences.

Example Prompt:

"Act as a cynical but brilliant senior engineer writing a 'pre-mortem' report.

Our New Project: We are about to start a project to build a real-time analytics dashboard that relies heavily on a new, third-party data streaming service called 'DataStreamer API'.

Your Task: Imagine it's six months from now, and this dashboard project has completely failed. Write a short pre-mortem report explaining what went wrong.

CRITICAL: In your report, you must draw direct parallels to the problems we faced in a past project, described below.

---

Past Project Failure Context: [Paste your anonymized data from the past failed project here] ---"

Why This Works

This technique forces the AI to move beyond generic failure modes. It will use the specific details you provided to create a chillingly plausible scenario.

Expected AI Output:

"Pre-Mortem Report: Project Dashboard

As predicted, the project is a failure. The cause is a repeat of the same mistake we made on [Project Zebra]. We staked the entire project's success on the 'DataStreamer API' without a proper technical spike or a backup plan. Just like last time, the third-party service was unable to handle our load. The dashboard is slow, the data is unreliable, and we've spent the last month trying to patch a sinking ship. Our failure to learn from the past has cost us another project."

This powerful, narrative-driven output makes the risk feel real and urgent. It provides you with a clear, high-priority risk to address immediately: thoroughly vet the DataStreamer API before committing the entire project to it.