5. Prioritizing Quality Data From The Start

Source:https://pair.withgoogle.com/guidebook/patterns

The foundation of any successful AI project is good data. Poor data management can lead to “data cascades,” where one issue leads to another, impacting the user experience. Investing in robust data practices early on can prevent these pitfalls.

Aim For:... See more

4. Being Prepared for Errors

Source:https://pair.withgoogle.com/guidebook/patterns

Errors are inevitable in any system, AI included. Planning for these hiccups from the start is crucial. Consider the potential errors, their impacts, and how you’ll address them. Will you offer manual overrides? How about direct access to customer support? These are... See more

2. Setting Clear Expectations

Source:https://pair.withgoogle.com/guidebook/patterns

AI isn’t always going to get it right, and that’s okay. What’s important is letting users know what to expect. Be honest about what your AI can do and where it might stumble. This honesty won’t just prevent frustration; it builds trust over time.

Aim For:

Be upfront... See more

3. Focusing on Benefits, Not Features

Source:https://pair.withgoogle.com/guidebook/patterns

Users care about how your product can make their lives better, not the technical wizardry behind it. When introducing your AI-powered service, highlight the advantages it brings to their everyday experiences.

Aim For:

Showcase how the tool adapts to their... See more