I had a client come to me a few months back with what they called a “total disaster.” They’d spent a fortune having a junior dev integrate a GPT-powered search assistant into their high-traffic WooCommerce store. On paper, it was supposed to be the ultimate shopping concierge. In reality? It was confidently hallucinating product features and promising 90% discount codes that didn’t exist. Customers were livid, and the support team was drowning. Total nightmare.
My first instinct—and I’m a bit embarrassed to admit this—was to just double down on prompt engineering. I thought I could just “jailbreak-proof” the system with a massive system prompt and more restrictive JSON schemas. That worked… for about a day. Then a user found a way to trigger a “helpful” response about a competitor’s pricing. The real problem wasn’t the LLM’s logic; it was that we hadn’t designed for user trust in AI. We were presenting an opaque oracle instead of a transparent partner.
The Four Pillars of Calibrated Trust
In the trenches of WordPress development, we often think about trust as “security” or “uptime.” But with AI, it’s more psychological. There’s a great concept I saw over at Smashing Magazine about the “four-legged stool” of trust: Ability, Benevolence, Integrity, and Predictability. If you’re building an AI feature and ignore one of these, the whole thing falls over.
Take predictability. If your AI assistant gives two different answers to the same question within five minutes, your user is going to bail. They can’t form a mental model of how the tool works. For my client, we had to move away from “blind trust” and toward what’s called calibrated trust. This means the user knows exactly where the AI shines and where it’s likely to trip up. Trust me on this: honesty about limitations is actually a feature, not a bug.
Implementing Confidence Thresholds
One way I fixed the “hallucination” issue for that WooCommerce store was by implementing a confidence score check before showing anything to the user. Instead of just dumping whatever the model spat out, we used the logprobs from the API to measure how “sure” the model was about its tokens. If the score was too low, we didn’t show the response. Simple. Effective. No more fake coupons.
/**
* bbioon_check_ai_confidence
* Simple logic to filter AI responses based on a dummy threshold.
*/
function bbioon_validate_ai_response( $response_data ) {
$threshold = 0.85; // We want 85% confidence
// In a real scenario, you'd parse logprobs from your OpenAI/Anthropic call
$confidence_score = $response_data['average_logprob_score'];
if ( $confidence_score < $threshold ) {
return [
'status' => 'error',
'message' => 'I\'m not quite sure about that. Let me connect you with a human.',
'fallback' => true
];
}
return [
'status' => 'success',
'content' => $response_data['text']
];
}We also added a “Show Sources” toggle. If the AI recommended a product, it had to link to the actual WooCommerce product ID. This hit the “Integrity” pillar. We weren’t just saying “Trust us,” we were saying “Verify us.” And that was it. The support tickets dropped by 40% in a week.
So, What’s the Point?
Building AI features isn’t just about the API call. It’s about the bridge between the machine’s output and the human’s expectations. Here’s what you need to remember:
- Stop aiming for 100% trust. Aim for calibrated trust where users know the limits.
- Handle errors with humility. An “I don’t know” is better than a confident lie. Period.
- Show your work. Use explainability (XAI) so users see the logic behind the suggestion.
- Measure the “Correction Rate.” If users are constantly editing what the AI generates, your “Ability” pillar is cracked.
Look, this stuff gets complicated fast. If you’re tired of debugging someone else’s mess and just want your site to work, drop my team a line. We’ve probably seen it before.
Are you designing for confidence, or are you just crossing your fingers and hoping for the best?
Leave a Reply