Vibe checks do not scale. Here is how to score a WordPress AI agent on five measurable dimensions, accuracy, reliability, latency, cost, and business impact, using golden datasets and the LLM-as-a-judge pattern to move from fragile demos to production.
Instructions alone will not give a model an identity. Three ways to train one persona into a small model, and why first-person statements generalized better than chat demonstrations or factual documents.
How to move past vibe coding to a spec-driven agentic AI workflow for production WordPress: persistent specifications, guardrails against silent failures, and automated tests, using tools like CodeSpeak and the Claude API.
Why AI performance problems usually trace back to inference architecture, not the model: retrieval quality, context compression, compute routing, and speculative decoding, with fine-tuning often a distraction.
A practical workflow for Claude Code: use Plan Mode before it touches your code, keep a CLAUDE.md skill file so fixes stick, and keep the context lean. The goal is production-ready WordPress, not AI-generated technical debt.
The WooCommerce integration for Claude adds 18 prebuilt AI workflows that behave like a senior analyst inside your store, from revenue triage to inventory risk reviews. They run as slash commands over live data, not static charts.
Semantic search alone misses exact technical terms. Combining BM25 with dense vectors, then re-ranking the top results with a cross-encoder, is what made our production RAG accurate. RAGAS metrics show the difference.
WordPress AI 0.9.0 adds a comment moderation experiment, bulk alt-text generation over WP-CLI, and per-feature model controls. Here is what each one does and how to test it before the 1.0.0 release.
WordPress 7.0 ships the WordPress AI Client, a standardized PHP API for generative AI in plugins. This guide builds a provider-agnostic image generation tool for the Media Library using the prompt builder, support checks, and a REST endpoint.
The Titanic dataset is the classic first project in data science. Using Python, Pandas, and Seaborn, this walkthrough shows how gender, passenger class, and family size shaped who survived, and what that teaches developers about reading data.