FeatBit Blog
Practical notes on progressive delivery, experimentation, self-hosting, AI-native release engineering, and the operating habits behind safer software launches.
articles on building, releasing, and improving software.
Showing 121–132 of 273 articles
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A practical tutorial for AI product and platform teams that need an online eval flag to control exposure, collect evidence, and roll back weak candidates.
A practical guide to designing offline graders for AI prompts, models, RAG, and agents before a candidate reaches production users.
A hands-on guide for product and platform teams that want to test a candidate feature, AI behavior, or experiment design before live rollout.
A practical definition of model A/B testing, how it differs from offline evals and shadow tests, and when teams should use it for AI releases.
A practical tutorial for using judge models to score AI outputs, calibrate rubrics, and turn evaluation results into rollout decisions.
A practical answer for AI teams that passed a benchmark but still need controlled exposure, production metrics, guardrails, and rollback.
A practical buyer guide to feature flag platforms, native AI eval support, online evaluation workflows, and release-control criteria for AI changes.
A practical guide for teams evaluating custom AI judge capabilities, with release-control criteria, metric design, rollout risks, and FeatBit patterns.
A tutorial for AI product and platform teams that need offline evaluation results to stop risky prompt, model, RAG, or agent changes before exposure.
A practical overview for AI teams building an eval scorecard that connects model quality, live outcomes, guardrails, and release decisions.
A product-team guide to understanding AI evals, comparing vendor terminology, and connecting offline scores to rollout, experiments, and release decisions.
A practical architecture guide for teams that need to compare AI model routes with real traffic, reliable exposure evidence, guardrails, and rollback.
