When testing a new feature, it’s common to use Focus groups or to do A/B testing. Meaning you show or test various implementation alternatives with relevant customers and get feedback.
This is costly, slow and not part of your DevOps pipeline.
A step in the right way is creating Synthetic Personas, using prompts engineering to describe each Persona’s character, presenting them the feature using multi-modal LLM if necessary (like Figma screen prototypes).
And then, measuring the feedback from this virtual crowd with some Monte Carlo Simulation over the LLM parameters (like temperature) so before you prioritize this feature in the backlog, you get some feedback!
You can also change your existing customer’s profiles to assess impact on new markets, and I’ll bet you can also compute rough implementation complexity, as a setup to the planning meeting about the future’s future.
This is costly, slow and not part of your DevOps pipeline.
A step in the right way is creating Synthetic Personas, using prompts engineering to describe each Persona’s character, presenting them the feature using multi-modal LLM if necessary (like Figma screen prototypes).
And then, measuring the feedback from this virtual crowd with some Monte Carlo Simulation over the LLM parameters (like temperature) so before you prioritize this feature in the backlog, you get some feedback!
You can also change your existing customer’s profiles to assess impact on new markets, and I’ll bet you can also compute rough implementation complexity, as a setup to the planning meeting about the future’s future.
A start of embryonic implementation can be found here.
