Experiment design for future bets

Problem

The team has several high-uncertainty ideas for the extension's future — deeper AI-assisted authoring, visual dashboard builders, cross-IDE portability, embedded collaboration — but no way to validate them cheaply before committing major engineering investment. Without designed experiments (prototypes, A/B tests, user interviews with clickable mockups, instrumented feature flags), the team must either bet big on unvalidated ideas or avoid risk entirely. Designing validation experiments now creates a repeatable playbook for testing future bets with minimal cost, so investment decisions are informed by evidence rather than intuition.

Context

• The larger future bets for analyst authoring in VS Code/Cursor with preview, diagnostics, and assist should be validated with scoped experiments before they absorb major implementation effort or become roadmap commitments.
• This task should design the experiments, not run them: define hypotheses, success signals, cheap prototypes or evaluation methods, and the decision rule for what happens next.
• Expected touchpoints include apps/ide/vscode-extension/, preview/inspector runtime code, and extension docs/tests, opportunity/prerequisite notes, eval or QA harnesses where relevant, and any external dependencies required to run the experiments.

Possible Solutions

• A - Rely on team intuition to pick which future bet to pursue: fast, but weak when the bets are expensive or high-risk.
• B - Recommended: design lightweight validation experiments for the strongest bets: specify hypothesis, method, scope, evidence, and the threshold for continuing or dropping the idea.
• C - Build full prototypes for every future direction immediately: rich signal, but far too expensive for early-stage uncertainty.

Plan

1. Choose the future bets for analyst authoring in VS Code/Cursor with preview, diagnostics, and assist that are both strategically important and uncertain enough to justify explicit experiments.
2. Define the hypothesis, cheapest credible validation method, required inputs, and success/failure signals for each experiment.
3. Document the operational constraints, owners, and follow-up decisions so the experiment outputs can actually change roadmap choices.
4. Rank the experiments by cost versus decision value and sequence the first one or two instead of trying to validate everything at once.

Implementation Progress

Review Feedback

• [ ] Review cleared