The advice going around now is that you let an LLM design the architecture and sort out the details later. I keep inheriting the result, and it is usually slow and painful to maintain. I call it “AI slop”: code that reads fine until it meets a race condition or real production traffic. What fixes it is not a better prompt. It is a scientific methodology, the same one you would use to test anything else.
I recently inherited a WooCommerce project where the previous dev had prompted his way through a complex multi step checkout. Unoptimized SQL queries, transients that never expired. I could not prompt my way back out of that, so I treated it like a lab experiment instead. Skip the hands on testing and your technical decisions are just expensive guesses.
Working past the prompt
This is not something reserved for data scientists. It is ordinary senior engineering. When you have to pick between two Redis caching strategies, or decide whether to move off a legacy VPS onto a specialized WP host, asking an AI which one is better gets you a confident paragraph and no evidence. Write a hypothesis instead.
A scientific methodology workflow usually runs like this:
- The problem: name the actual bottleneck, as in “the cart fragments AJAX call takes 1.2s”.
- The hypothesis: state something you can be wrong about, as in “moving cart data to a custom table cuts response time by 30%”.
- Testing: build a proof of concept (PoC) that isolates one variable at a time.
- The conclusions: read the numbers even when they kill the idea you liked.
It also keeps technical debt in AI development from piling up, because the slop gets caught before it ever lands in your Git repo.
A proof of concept in practice
Say you are weighing the native WordPress metadata API against a custom table for a high volume logging feature. Rather than guess, write a benchmark script and measure two things: insertion rate, and query speed under load.
<?php
/**
* bbioon_benchmark_metadata vs custom table
* This is a simplified test script to validate a hypothesis.
*/
function bbioon_run_performance_test() {
$iterations = 1000;
// Test Metadata API
$start_meta = microtime(true);
for ($i = 0; $i < $iterations; $i++) {
update_post_meta(1, '_bbioon_test_log_' . $i, 'some_random_data');
}
$end_meta = microtime(true) - $start_meta;
// Output results for analysis
error_log("Metadata API: " . $end_meta . " seconds");
}
Collect several runs, account for the environmental noise from object caching, and you end up with a decision you can defend with numbers instead of a vibe from a chatbot. That is also how you keep the Iron Triangle of AI development honest: quality, speed and cost.
Separating signal from noise
AI slop hides incompetence behind tidy syntax. The gains that actually move a page load come from the unglamorous parts: handling transients properly, avoiding race conditions in the wp_options table, checking that your hooks are not firing when nobody asked them to. So run your scientific methodology at different times of day and under different server loads. That is where the hidden bottlenecks show up.
For the theory behind all this, this guide on PoC software engineering is worth your time. It matches how I handle high stakes WooCommerce refactors.
If this kind of testing is eating your dev hours, hand it to me. I have been wrestling with WordPress since the 4.x days.
The short version
Treat AI output as an unverified hypothesis, not as gospel. Whether you are navigating PHP versions or wiring up a custom headless integration, stability comes from doing the work by hand. Run a scientific methodology over every line an LLM hands you. You will notice the difference the first night the site does not break at 3 AM.
