In my previous post, I discussed how the best AI product teams use a double loop learning approach.
They constantly question assumptions, test ideas, and make changes based on feedback. This back-and-forth between technical development and subject-matter expertise is key to building AI products that work well and provide real value to users.
But there's another important idea to consider when it comes to AI products: alignment versus development.
In AI, "alignment" means making sure AI systems are designed to achieve the outcomes that are best for users and society. This ensures the AI's goals and values line up with those of the people it's supposed to help.
Development, on the other hand, is about the actual work of building the AI system. This includes things like designing structures, training AI models, and writing code.
Both alignment and development are crucial, but they require different skills and approaches.
Development is about technical skills and efficiency. It's about making the AI development process as smooth and fast as possible.
Alignment is more about the big picture. It's about really understanding the problem you're trying to solve, the people you're trying to help, and the potential unintended consequences of the AI system. It requires deep knowledge of the subject and careful thinking about ethics.
At Autoblocks, we want to help AI teams connect alignment and development. We believe the key is to link AI development to real user outcomes from the very beginning.
This means:
- Creating accurate tests and evaluations that can stand in for user outcomes.
- Connecting those evaluations to actual user outcomes and business goals.
- Keeping those evaluations accurate over time as the AI system changes.
- Using test cases that reflect real user inputs and interactions.
By closely connecting development and alignment from the start, we can create a more predictable process for building AI products. We can be more confident that our changes are actually leading to the user outcomes we care about.
This is still a difficult process.
User outcomes can take a while to show up, and there are often many competing factors at play. It takes a lot of work upfront to design the right evaluations and test cases.
But in a competitive market where many teams are racing to build similar AI products, this kind of outcome-focused development can be a real advantage. It lets teams move quickly and efficiently while still keeping the bigger picture in mind.
In the end, AI product alignment and development are two sides of the same coin. You need both to succeed. The best AI teams know this and create processes to keep both in sync.
It's not easy, but it's the key to building AI products that don't just work, but actually delight users.
And it underscores the importance of collaboration between technical and non-technical team members in this space. Only by working closely together can we hope to align AI with our values and goals.