Double Loop AI Product Dev
The idea is that when something goes wrong, you can either just fix the immediate problem (single loop learning), or go deeper and question the underlying assumptions and strategies that led to the problem in the first place (double loop learning).
It's a powerful framework for continuous improvement that has applications well beyond the realm of organizational learning.
You've got your developers and ML engineers (technical) on one side, and your SMEs, PMs, and CX folks (experts) on the other. The magic happens when you create a tight feedback loop between the two.
But it's not as simple as just putting everyone in a room together and hoping for the best. You have to be intentional about how you leverage each skill set.
The technical side is all about efficiency - streamlining the AI pipeline, running evaluations and tests, and making sure the machine is humming.
The expertise side is more about steering the ship - prompting the right questions, manually reviewing outputs, and making sure the product is actually solving the problem it's meant to solve.
