Speak to AI About Your Project

 

Project managers must embrace new technology, especially when it can improve project performance. Large language models (LLMs) such as ChatGPT, Bard, and llama can be excellent tools if used properly. As with most technology, we must learn how to effectively interact with this software application to optimize the results. Humans are inconsistent when asking questions.

“What is the weather today?”

“What is the temperature?”

“What is it like outside?”

“Is it going to rain or be sunny today?”

The words, phrases, and sentences people say are known as utterances. The LLM evaluates them to determine an intent. For the questions above, the intent is to get a report on the weather. How we ask questions determines the results obtained, and there are tips for using this process effectively. A new area of knowledge is being developed called prompt engineering, which is the ability to make a request to an LLM and obtain the best response.

Prompt Engineering Techniques

1. Chain prompting. This technique is based on LLMs being a conversational technology that remembers previous questions and answers. Based on this characteristic, after you ask a question and receive a response, you can modify your next question. Using this method, you can seek a change to the first response or use feedback to develop a better series of questions.

Project Scenario

Q1. What is the greatest risk to my project?                   

LLM answer 1. The project schedule.

Q2. Why is this such a big risk?                                     

LLM answer 2. There are resource issues where allocated resources have insufficient

experience to complete the tasks on time.

Q3. What is the best way to mitigate this risk?                

LLM answer 3. Perform an assessment for critical path tasks comparing task complexity to

resource capability.

Q4. Will there be residual issues if this risk occurs?        

LLM answer 4. If your schedule is late, there is the potential for additional risks that affect

product quality.

2. Persona replication. This has the potential to be the most exciting and the most dangerous feature of LLMs. By loading content from a specific individual, the LLM can assume the characteristics of the person and respond in that persona. For example, once you load a series of texts by a famous scientist, you ask the LLM to respond based on the manner and knowledge of that person.

Project Scenario

In an agile project, customer feedback is an important factor for iterations. The project manager can load information about the customer (with their permission) to acquire feedback when the customer is unavailable. The process is to load personal background information, experience, organizational responsibilities, emails, messages, and previous decisions. When a sprint is completed, the project manager asks the LLM to respond in the voice of the customer (VoC).

3. Chunking. Sometimes, you need a long response, and the best approach is to break it into smaller segments. For example, you want the LLM to write a movie screenplay. Rather than providing the basic plot and characters and then letting it create an entire movie script, it makes more sense to ask for the first few scenes. Based on the initial response, you can modify the parameters before you ask for the next series of scenes. Chunking is the process of accomplishing your request using a step-by-step approach to provide a better result.

Project Scenario

Instead of asking for an entire project plan, the project manager provides the project type and objective then requests a plan for the first stage, such as design. After reviewing the results, the following request is for details on the implementation stage. Similarly, rather than asking for an entire project management plan, the project manager asks for a sequence of components such as a risk plan, resource plan, and communication plan.

4. Response customization. Additional features known as temperature and frequency penalties allow you to alter the randomness of a response and the number of repetitive words or phrases.

Conclusion

LLMs offer a myriad of capability that has not yet been fully exploited. For example, a project manager can create a status report or an important message and ask the LLM to modify it to eliminate bias or improve clarity. Learning how to collaborate with this technology using prompt engineering techniques improves the project results and the performance of project managers.