Lessons Learned from Building, Launching, and Delivering an AI Product

Today building AI-powered products is no longer just about adding a chatbot or a few smart features. It’s about fundamentally rethinking how we design, build, and deliver products that solve real problems and delight users. Here are the key lessons I’ve learned from my journey with the Back4App AI Agent.

Here at Back4App, we created our AI Agent in the Fall of 2023 with the initial goal of delivering value by abstracting the DevOps process. Our aim was to deliver something transformational that could help you ship code faster to production.

What we found is that users need their problems solved efficiently. Many people told us they had a hard time creating effective, detailed prompts, so we realized the problem started even earlier. Here are some lessons I’ve learned while creating this AI Agent product that I think may help you create your next AI products or even incorporate AI into your existing apps.

Think Differently

Building products with AI requires a shift in mindset. It’s not just about tacking on AI features; it’s about integrating AI into the core of the product experience. This demands a deep understanding of both the technology, its potential applications, and mainly, its limitations.

For example, LLMs (Large Language Models) are AI models trained to predict the next word. Imagine how your users’ workflows could be improved with such a tool.

Solve Real Problems

While impressive demos of AI capabilities can capture attention, the true value lies in solving real problems for users. The key is to ensure that your AI solutions are not only technically advanced but also genuinely useful and loved by your customers. Focus on problems that can be effectively tackled and solved by AI, resulting in meaningful impacts on users’ lives.

Prioritize Product Design and UX

The promise of AI—to create anything with just a few words—can be overwhelming. It’s crucial to provide clear starting points, build user confidence, and ensure an intuitive product design. Continuous education and support are vital to help users adopt and adapt to new AI-driven functionalities.

At Back4App, one thing that improved our user experience was providing sample prompts and full conversation examples to guide interactions with our AI Agent.

Leverage Proprietary Data

In a world where AI models (Data > Code) are becoming commoditized, your data and interfaces can be your strongest assets. AI products that utilize proprietary data sets and feature superior interfaces will have a competitive edge. Invest in collecting and refining unique data that can enhance your AI’s performance and relevance.

Intentional Initial Workflows

Start with workflows that feel like achievable tasks but offer high rewards when executed well. This approach encourages repeated use and demonstrates significant time savings, motivating users to integrate your AI solutions into their regular routines.

Embrace the “AI-Powered” Label

Branding your product as “AI-powered” can boost initial engagement. It helps users understand the capabilities and potential of the feature, setting the stage for higher interaction and adoption rates. Make sure the branding reflects real, tangible benefits that users can experience.

Focus on Small, Impactful Improvements

Small AI enhancements, such as auto-completing names or simple data transformations, can have a larger impact than more complex features like chatbots. These small improvements are often easier for users to understand and adopt, leading to greater overall satisfaction and productivity gains.

Customization and Continuous Refinement

AI products must be highly customizable to work well across different use cases. Users are generally willing to spend time tweaking prompts and experimenting with models to achieve better results. Providing tools and support for this customization is crucial for long-term success.

Continuous Improvement of Core Models

The underlying AI models are constantly evolving. Products that are built to leverage these continuous improvements will perform better over time. Stay up-to-date with advancements in AI technology and integrate these updates into your product to maintain a competitive advantage.

Plan for Scalability

From the outset, it’s essential to plan for the scalability of your AI systems and infrastructure. Anticipate and design for increased user demand to avoid performance bottlenecks. A scalable architecture ensures that your product can grow and adapt as its user base expands.

Reduce Time in Apps

The best AI features enhance productivity by reducing the time users spend in your app, giving them back valuable time. Unlike previous generations of AI/ML that often increased screen time, today’s AI should focus on efficiency and effectiveness, helping users achieve their goals more quickly.

Conclusion

Integrating AI into products effectively requires a down-to-earth approach that balances innovation with practicality. By focusing on solving real problems, prioritizing user experience, leveraging proprietary data, and planning for continuous improvement and scalability, you can build AI products that not only impress but also deliver lasting value.

Do you have some additional learnings that would like to share?