How to save considerable time in product development work with the help of GPT

Decomposing user stories, or breaking down large, complex stories into smaller, more manageable chunks,
is a common practice in agile software development. It allows teams to better understand and estimate the work involved,
create focused tests, and get feedback from stakeholders more quickly.

Decomposing user stories is a challenging skill, a real art that needs to be learned.
The decomposition must be based on the value of the feature, which is not easy at all,
especially when we’re learning it for the first time..

In agile infrastructures, story breakdown is often insufficient or becomes a time-consuming process.
Thinking time, grooming time, rewriting inflate the time it takes to create good stories.
One tool that can be useful in decomposing user stories is OpenAI’s GPT-3 language model.
GPT-3 is a powerful artificial intelligence that can generate human-like text based on a given prompt.
It can be accessed through the Chat.OpenAI API, which allows developers to easily integrate GPT-3 into their workflow.

The idea is to use GPT-3  in order to speed up the breaking down large, complex stories into smaller, more manageable chunks, process,
provide the product person with additional options and better prepare the grooming in a shorter time.
It’s not magic, and doesn’t replace human creativity right now, it’s just a huge time saver.

To use GPT-3 for decomposing user stories, developers can provide the model with a high-level user story and ask it to generate a list of smaller,
more specific stories. For example, if the high-level user story is “As a user, I want to be able to search for products on the website,
” the developer could ask GPT-3 to generate a list of smaller stories such as:

• “Implement a search bar on the homepage”
• “Create a search results page”
• “Allow users to filter search results by price, rating, and other criteria”

GPT-3 can also be used to generate acceptance criteria for each of the smaller stories.
This can help the team better understand the specific requirements and ensure that the story is complete and ready to be worked on.

Overall, using GPT-3 to decompose user stories can save time and effort for development teams.
It is a powerful tool that can help teams break down complex work into smaller, more manageable chunks and stay organized and focused on delivering value to users.

Imagine you are working on an online education platform and have the following user story:

“As a student, I want to be able to access course materials and submit assignments online so that I can complete my coursework from anywhere at any time.”

This user story is quite broad and could involve many different tasks and sub-tasks. To decompose this user story using GPT-3, you might do the following:

1. Access the Chat.OpenAI API and provide it with the following prompt:
   “Please generate a list of smaller user stories based on the following high-level user story: ‘As a student,
   I want to be able to access course materials and submit assignments online so that I can complete my coursework from anywhere at any time.'”
2. GPT-3 might generate the following list of smaller user stories:

• “Create a course materials page that displays all of the documents and resources associated with a course”
• “Allow students to download course materials as needed”
• “Implement a submission system for assignments that allows students to upload their work and receive feedback from instructors”
• “Allow students to view their grades and feedback for completed assignments”
• “Add a calendar feature that shows the due dates for assignments and other important course dates”

3. You can then use GPT-3 to generate acceptance criteria for each of these smaller user stories.
For example, you might provide GPT-3 with the following prompt for the first user story:
“Please generate acceptance criteria for the following user story: ‘Create a course materials page that displays all of the documents and resources associated with a course.
‘” GPT-3 might generate the following acceptance criteria:

• “The course materials page should be accessible from the student dashboard”
• “The course materials page should display all documents and resources in a clear and organized manner”
• “The course materials page should allow students to search for specific documents or resources”
• “The course materials page should display the file type and size of each document or resource”

This process can be repeated for each of the smaller user stories generated by GPT-3,
helping to ensure that all of the required tasks are identified and that each user story is well-defined and ready to be worked on by the development team.

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