Vibes-checking AI output is the new bottleneck. What actually moved my Claude Code performance: terminal permissions for the agent, a test requested with every feature, and a no-stop rule until they pass.
A static A/B test keeps sending half your traffic to the losing variation until someone stops the test. Thompson sampling moves the traffic as the numbers come in. Here is the Beta distribution behind it and how to store the state in WordPress.
An ad hoc prompt works once, then quietly stops matching the result you got the first time. Claude Code Skills wrap the workflow in a SKILL.md file, with structured JSON for the data and Python for the parts that have to be deterministic.
Retrieval gets worse as the memory pool grows, while the confidence score keeps climbing. Here is what causes RAG memory decay and the four fixes I put into production systems: topic routing, semantic deduplication, relevance based eviction, and lexical reranking.
Recruiters now expect designers to ship production code out of AI agents. Here is what that costs in practice: toggles no screen reader can see, XSS holes in generated forms, duplicated code, and engineers spending more time per pull request cleaning up after it.
Basic embeddings only get you so far. Ahmad Wael explains the two-stage pattern behind advanced RAG retrieval: a bi-encoder for recall, a cross-encoder for precision, and the caching tricks that keep the second pass off most of your traffic.
Asking a chatbot for a WooCommerce snippet is a lever pull, and the losing pulls are the hard ones to spot. Where LLM generated code breaks WordPress sites, what it does to coding standards, and why per-token pricing makes it worse.
Cowork gets written off as Claude Code for people who don’t code. I use it for architecture work and multi-file debugging, and this is how I keep the context window lean, why I stay in plan mode, and which repeat jobs I turned into skills.
Connecting Claude to a WordPress site over the Model Context Protocol turns your posts and comments into something it can read. Here is the setup, the gap-analysis prompt I use, and how I score the ideas that come back.
Standard vector RAG shreds documents into contextless fragments. Proxy-Pointer RAG keeps the heading hierarchy through skeleton trees and breadcrumbs, and on 66 queries against 10-K filings every number matched the ground truth.