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AI-Powered Product Development
Practical notes on frontend craft, architecture, and shipping fast web experiences written by the Crexed team.
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Most AI PRDs list features instead of measurable behavior. This post gives a practical PRD shape: testable requirements, eval metrics, data and failure-mode specs, and rollout plans so product and engineering align before you train or ship.
Pick an architecture for the job: control for policy-heavy automation, adaptability for ill-defined work, or a hybrid that plans broadly and acts narrowly. Side-by-side traits, examples, and questions to choose with confidence.
Slow AI feels broken before wrong AI does. This playbook covers streaming, smart caching keyed by user and TTL, progressive UI shells, and how to measure where time actually goes so you fix the right bottleneck.
Not every problem has a fixed script. NemoClaw describes agents that replan as they learn, with clear trade-offs (debugging, variance) and patterns limits, evaluation, separation of planning from execution to keep adaptive AI governable.
RAG bugs often look like “the model lied” when retrieval or docs are wrong. Learn separate metrics, a simple weekly eval set, structured failure tags, and a step-by-step debug order: retrieval first, then chunks, then sources.
