Clients keep asking for AI product search on stores where the catalog is already a database. Before anyone writes an embedding job, run the plain query and see what it answers. Here is where RAG genuinely fits on a store, and where a filter wins.
An agent answering store questions is only as good as the definitions underneath it. Before wiring one to a WooCommerce database, know where the numbers actually come from: post tables or HPOS, lookup tables or raw orders, and what revenue even means.
Adding a cross-encoder reranker to store search sounds like the obvious upgrade. The tests I read suggest it rarely beats a better embedding, costs hundreds of milliseconds per query, and only clearly wins when answers sit buried in long text. Filters, synonyms and SKU lookups come first.
Microsoft Fabric’s data agent lets people ask a report questions in plain language and get numbers back. The pattern is heading for WooCommerce, but it assumes a modelled reporting layer most stores don’t have yet. The real work sits below the chat.
A driving-video eval study found a judge model with strong correlation that still caught only 2% of failures, because it never used the ends of the scale. If your plugin leans on model-graded output, the same trap is waiting, and it is cheap to check.
Syntax checks pass, dev copies pass, and the client’s export still comes back empty. This is my take on metacognitive regulation for developers: turn the self-checks into steps you run before you trust what the model wrote, and know when to skip them.
Most advice on running agents at once is about attention: overview screens, alerts, recaps. On WordPress client work the shared checkout and the shared database break first. This is how I split the work, and when I stick to one session.
A chat model writes tidy solver code for five products and falls over on a real catalog. ORPilot interviews the user first and keeps raw data out of the prompt, and that pipeline is worth copying for AI features on a WooCommerce store.
An AI pass over your products can finish with valid JSON and no errors while it quietly corrupts the data. A better prompt won’t fix that. The queue WooCommerce already ships will: one item per action, retries, checkpoints, and output you can audit with a script.
An AI feature that works for three turns and then dies with a context length exceeded error has a design flaw. The transcript was never a database. Here’s where the run log belongs and how to keep prompt caching working.