Run with Browser AI · Chapter 14

Run Chapter 14: Feature Evaluation and Release Gate

Run seven canonical Browser AI fixtures with slices, deterministic baselines, repetitions, paired outcome transitions, human review and risk-based release gates.

← Read Evaluate the Feature, Not the Demo
Execution Not inspected
Secure contextUnknown
Top-level pageUnknown
Prompt APIUnknown
AvailabilityNot inspected

Chapter experiment

Evaluate the Feature, Not the Demo

Metrics-only trace

Loading the chapter experiment…

No model output yet.
Inspect the context supplied by this chapter

What this experience knows

This experience is grounded in Chapter 14 and evaluates feature contracts rather than isolated model calls.

Feature boundary

Each case identifies the user-facing feature, Browser AI interface, creation options, generation input, evaluator-only assertions, human rubric, slices and risk class. The model is one component of that path.

Leakage boundary

Generation input and evaluator expectations are stored separately. Assertions, reference outcomes and human rubrics are not appended to live API input.

Repetition boundary

Every repetition remains a raw observation. Failed runs are not replaced with successful retries, and small sample counts are described as smoke evidence rather than probability estimates.

Baseline boundary

The paired baseline is deterministic and explicitly non-AI. Some baselines abstain or preserve the original rather than manufacture text. The relevant comparison is the complete feature route, not AI versus nothing.

Slice boundary

Aggregate results are accompanied by slice results. Authority, permission and data-route slices receive a zero-new-regressions gate.

Review boundary

Automatic assertions have limited scopes. Every live candidate also carries one visible human rubric with Pass, Fail and Unclear; pending and unclear reviews do not become passes.

Release boundary

The gate requires classified API states, metrics-only capture, completed live observations, decided human rubrics, no high-risk regression and an explicit comparison label. Passing protects only this maintained fixture set.

Trace boundary

The reviewed Chrome trace contains capability and session-creation evidence but no terminal prompt. Replay never converts that missing behavior into an evaluation result.

This laboratory turns the book’s seven Browser AI interfaces into one canonical smoke suite. It keeps raw repetitions, joins operational and behavioral evidence by run ID, compares every current outcome with a deterministic non-AI baseline, reports slice results and computes a small risk-based release gate.

Human rubrics remain pending until a reviewer decides them. A high aggregate score cannot hide a new authority or permission failure.