Begin To Avoid These Now & See a Difference 1️⃣ Not Defining a Clear Purpose for Your AI Too often, people dive into building an AI without fully understanding what problem it’s meant to solve or who it's meant to help. A clear goal is crucial. Are you building a voice assistant for customer service? A personalized AI tutor? Define the why behind your AI first—this will shape your decisions about design, functionality, and user experience. 2️⃣ Skipping the Testing Phase (or Just Doing It Once) Testing isn’t a one-and-done process. If you don't test multiple times, you're setting yourself up for failure. Make sure to test your AI across different scenarios, with different accents or languages, and in various environments (quiet vs. noisy). The more data you collect, the better your AI will perform. Aim for quality assurance, not just functionality. 3️⃣ Ignoring Real User Feedback During Testing It’s easy to fall into the trap of relying solely on internal testing or theoretical use cases. But without actual user feedback, you miss key issues that could affect performance, understanding, and satisfaction. Let real users interact with your AI—whether they’re your target customers or just people from different backgrounds. Their feedback is invaluable. What are other mistakes you are seeing or issues you are facing 👇

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