Once the project has been defined and the data has been acquired and curated, it is time to start creating a prototype of the solution. The prototype will serve as a preliminary model of the solution and enable the project stakeholders to provide early feedback and course corrections as needed. Additionally, building a prototype forces a reality check on the project as a whole, since the prototype will necessarily be a vertical piece of functionality, testing that most elements of the technology stack work together.
Assuming your user stories had an initial prioritization set during the project planning step, your prototype should implement the top few user stories. In this way, you will start to see business value as soon as the initial prototype is complete. Note that the combination of user stories you tackle during this phase should leverage most of the technical components of your planned system. For instance, if using training data from a particular data source will be critical to your system's success, you should ensure that at least one of your user stories selected for the prototype step requires that data.
Is There an Existing Solution?
Before you spend resources going down the path of building an AI solution, the first question you should ask yourself is “Can my problems be solved by an existing solution?” For instance, if a business wants to have a simple automated chat capability for their customers, an all-in-one solution likely exists that ...
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