Efficiency is one slice of AI value, not the whole thing. Where automation, augmentation and innovation each show up, why the accuracy tax cancels out saved hours, and how to queue an AI fraud check so it never blocks checkout.
Prompt caching gets all the attention while the expensive work happens before the model sees a token. Five places to cache in a RAG pipeline, from query embeddings to full query-response pairs, with a transients example for WordPress.
The API bill is the usual reason teams look at their own hardware, and privacy is the better one. Which benchmarks to trust for agents, how far you can quantize before logic breaks, what a single A100 on GCP costs, and serving Qwen 3.5-27B with vLLM.
WordPress.org now runs an MCP server for the plugin directory, so an AI assistant can validate readme.txt, check where a submission sits in the queue and read the guidelines from the source. The review team still holds you responsible for the code.
Telex is Automattic’s experimental tool for generating block themes from a prompt. What the output actually contains, the templates you have to ask for, and why I test every generated ZIP in Playground first.
The Connectors API gives WordPress one registry for external services, starting with AI providers. Notes on the init lifecycle, the unregister and register dance for overrides, and the keys that sit unencrypted in wp_options.
AI coding assistants write PHP that reads fine and falls over in production. A WooCommerce audit, the naive fetch they keep producing, and the refactor I run it through.
Gemini Embeddings 2 is in public preview: one vector space for text, images, audio and video. The preview limits, calling it from WordPress with the HTTP API, and why cosine similarity on its own still is not enough.
Prompt caching takes up to 90% off input token cost and around 80% off latency, but only when the prompt prefix never moves. Here is how the pre-fill stage works, where the 1,024-token threshold sits, and how to order a prompt so it hits the cache.
Building an AI application is mostly plumbing: keys kept out of the repo, a plan for 429 responses, and a chunking engine for anything longer than the token limit. Notes on what keeps an AI feature running once it leaves your laptop.