Something is off in how the WordPress ecosystem is adopting AI. Developers and business owners are shoving LLMs into checkout flows and support bots without a thought about AI Project Evaluation. It is like writing a custom WooCommerce payment gateway and hoping it charges the right amount. Hope is not a plan, and in a professional environment it costs you time you do not have.
I have been wrestling with WordPress since the 4.x days, and messy planning has always produced messy code. Traditional software is deterministic: X happens, Y follows. LLMs are probabilistic, so the same input can hand you a different answer twice in a row. Start building without a plan for measuring success and you are just vibe coding.
The illusion of “it seems okay”
The mistake I see most often is ad hoc, qualitative testing. A developer runs three prompts, gets a decent answer each time, and ships. What happens on the hundredth prompt, or the first time the user input arrives slightly malformed? Knowing whether your AI works takes a systematic AI Project Evaluation plan.
Without KPIs (Key Performance Indicators) you are guessing, and spot checks will not hold the line. Identify the usage scenarios that matter, write tests that capture them, and run those tests often enough to see the spread of results. “Nobody’s complaining” is not a success metric. Most users never complain, they just leave.
Setting goalposts and measurement validity
Measurement validity is one of the harder parts of AI Project Evaluation: the gap between what you can measure and what actually matters. A recent discussion on Hacker News got into the mismatch between non-deterministic LLMs and enterprise systems that expect deterministic behavior. Response time on its own tells you almost nothing.
It is like judging health by BMI alone. Cheap, easy, and nowhere near the whole picture. Break the vision down into objectives small enough to measure. Pick your KPIs after the build and you will drift toward metrics that are easy to hit rather than the ones that move the business.
Managing nondeterministic risk
Because the same input can produce different outputs, decide your risk tolerance early. That decision is a real part of AI Project Evaluation. Work out the failure modes of your model, whether that is a hallucinated answer or a tool called the wrong way. I went into this in the post on stopping AI hallucinations, where most of the fix comes down to context.
For that, build a logging layer that captures prompt and response pairs for offline analysis. Here is the basic pattern I use in WordPress to log AI interactions:
<?php
/**
* Simple Logger for AI Project Evaluation
* Captures LLM interactions for later audit and scoring.
*/
function bbioon_log_ai_interaction( $prompt, $response, $metadata = [] ) {
global $wpdb;
$table_name = $wpdb->prefix . 'ai_evaluation_logs';
// We use a custom table because transients are too volatile for evaluation data
$wpdb->insert(
$table_name,
[
'prompt_hash' => md5( $prompt ),
'raw_prompt' => $prompt,
'raw_response' => is_string( $response ) ? $response : wp_json_encode( $response ),
'meta_data' => wp_json_encode( $metadata ),
'created_at' => current_time( 'mysql' ),
]
);
}
?>With those logs you can run golden set testing, comparing a new model version against responses you already know are good. That is how you get from vibes to verification.
If this AI Project Evaluation work is eating your dev hours, I can take it off your plate. I have been wrestling with WordPress since the 4.x days.
Plan before you code
Evaluation matters more for AI projects than for ordinary software because the models are unstable by nature. Getting value out of one takes close scrutiny and an honest plan for the day the LLM does something strange. Don’t write a line of code until you know how you are going to prove it works.
