Golden Rule of Building AI Products: Walk Before You Run

I've been on a team that burned time and resources building an AI product that missed the mark. Not because the idea was bad, but because we tried to sprint before learning to walk. Through research, dissecting success stories, and examining our own missteps, I've distilled five key principles that became a lifeline. Transforming our AI product development from a series of frustrating dead-ends to a focused, strategic approach that ultimately delivered real value. These aren't theoretical guidelines, just lessons learned from building, breaking, and rebuilding an AI product. Hopefully these will help you avoid the same mistakes.

1. Look at Your Data

Continuously review your user data, or generate synthetic data if you don't have user data yet. Cluster this data to identify subtopics and assess your system's performance across different areas. By analyzing which subtopics your AI handles well or poorly, you can strategically focus your improvement efforts. This data-driven approach helps you understand your model's strengths and weaknesses, allowing you to optimize the product where users are most actively engaging and where performance can be most significantly enhanced.

2. Always Make Evidence-Based Decisions

Now that you've clustered and analyzed your data you're equipped for evidence-based decision making. For each system improvement, establish a measurable method to validate actual progress. Relying on gut feelings, vague impressions, or anecdotal observations will prevent meaningful advancement and frankly, will lead nowhere. Quantifiable measurement is the only path to understanding and improving your AI product's performance. Remember that AI performance metrics must ultimately align with business outcomes, a technically impressive AI improvement means little if it isn't rooted in business outcomes.

3. Quality Over Quantity

It is so easy to list out potentail cool features for your product during internal discussions. However, what is cool and useful to you might not be what your paying users find cool and useful. Now that you are making evidence-based decisions you've identified what your users actually want and can target your efforts toward making quality improvements in those areas. This is a far stronger strategy than spreading yourself thin building 10 half-baked features that you have no evidence of your users even wanting.

4. Repetition Over Perfection

You've identified the highest ROI task to work on, now you prioritize a workflow that emphasizes rapid iteration and experimentation over perfectionism. In this fast-moving field, your codebase must be flexible enough to quickly test and integrate new research without major structural changes. Build systems that allow you to swap components and experiment quickly, ensuring your product can evolve as quickly as the underlying AI technology. There's no need spending weeks trying to get some specific model working if there's another that already works out of the box.

5. Your AI Product is Fundamentally Still a Product

You are in a great spot, working on things quickly and working on things your users want. Now don't forget you are still developing a product. Everything that applies to developing any other product still applies here. While AI capabilities are crucial, user experience and interface design are equally critical. An amazing AI solution hidden behind a poor user interface will fail to attract users. Prioritize creating an intuitive, visually appealing, and easy-to-navigate product that makes complex AI technology feel accessible and seamless for your target audience.

Conclusion

Walking before running isn't about moving slowly, it's about being calculated and waiting until you are ready to run without falling on your face and being scared to run again. These five principles work together to ensure you're learning, iterating, and solving real user problems. I hope this post brought you some value, if it did consider sharing it with someone else who might find it useful.

Building an AI product is easier when you have a plan. If you're in the early stages and want to set a strong foundation, let's chat! Schedule a meeting here