Comparing PMI Infinity Responses to Three Other Large Language Models

 

Here is a comparison of large language model (LLM) responses to the same prompt. The scenario is a project manager asking for suggestions to recover a project schedule from being late. The input is simple as if the project manager had no time to perform extensive prompt engineering or provide specific current and historical project data. The comparison was made on the same day (June 20) for all LLMs.

The first row is the LLM used. The second row indicates the number of steps in the suggested process, although some had sub-steps or additional suggestions. The recommendations are listed with similar content in the same row. Although the responses are summarized, the wording is intended to reflect the responses as accurately as possible.   

INPUT: “My project completion date is late to schedule. How can I recover the time?”

The table is a summary of each set of responses.

Observations

According to PMBOK 7th ed., the two main schedule compression methods are crashing and fast tracking. Crashing is a specific technique that determines the most effective schedule reduction at the least cost for tasks on the critical path. Fast tracking evaluates if tasks normally performed in sequence, a finish-to-start relationship, can have some or all of a task done in parallel with another task on the critical path. None of the LLMs suggested both of these. CoPilot was the only LLM that used the term “crashing” the schedule. It was disappointing that PMI Infinity did not include this. PMI Infinity and ChatGPT included risk management. ChatGPT and Gemini were the only LLMs that responded with a social concern for the well-being of the project team.

It is important to remember that LLM responses, like those of other AI-based solutions, are determined by the data accessed. From my perspective, ChatGPT offered the most comprehensive recommendations, and CoPilot had the weakest response.

What observations do you think are relevant in this comparison?