AI Product Readiness Assessment

We can launch now with a 'Beta' label that provides a disclaimer about the accuracy rate. This way we preserve credibility and trust with our customers, and continue to be fast-to-market.

Reducing hallucinations and increasing accuracy is important for building trust with our customers. Taking extra time to improve accuracy is the best option.

We can launch with grounding measures (e.g., citation links to source docs) and add specific UI flows for users to provide feedback on the errors, hopefully turning the failure cases into training data.

THE QUANTUM COLLECTIVE

THE QUANTUM PRODUCT MANIFESTO

You are preparing to launch a feature that answers questions with factual data. Your internal testing shows accurate responses 88% of the time. The engineering team says getting to 95% will take another 4 months. What is your go-to-market decision and strategy?

You have a chatbot feature designed to answer a user question, using existing data in the standard models. How can you make your solution unique in a way other competitors can't match?

Our new AI feature is a hit, but our bill from our model provider has skyrocketed, making the feature gross margin negative. How could we reduce costs without removing the feature?

In traditional software, we have 'Pass/Fail' integration tests. How do you define 'Done' for a text-generation feature where the answer changes every time? What artifacts do you need to see before approving a release?

Your model takes 6 seconds to generate a full answer. Users are bouncing because it feels frozen. Engineering says the latency cannot be reduced. How do you solve this using Product/UX design?

Your Assessment Results

THE QUANTUM COLLECTIVE