We need to talk about the “LLM-as-judge” trap. For some reason, the standard advice to detect LLM hallucinations has become asking another language model to verify the output. Furthermore, it is like asking a colorblind person to sort paint samples—it is circular, expensive, and frankly, lazy engineering. If you are building production-grade AI features in WordPress, you cannot rely on a system that hallucinates to police itself.
I have spent years debugging race conditions and legacy PHP hooks, but AI adds a new layer of “broken” that does not show up in the error logs. Therefore, we need a deterministic, geometric way to spot when a model is lying. Specifically, we can look at the underlying vector space where these models live.
The Geometry of a Lie
When you feed text into an embedding model, you get a vector—a point in high-dimensional space. Semantically similar texts land near each other. However, there is a deeper structure than just distance. Specifically, if you connect a question vector to its answer vector, you get a “displacement vector.”
In a grounded, truthful system, these displacement vectors point in consistent directions within a specific domain. Consequently, if you ask five legal questions, the vectors connecting them to their answers should be roughly parallel. When a model hallucinates, the response might still sound fluent, but its displacement vector points in a totally different direction. It is the “red bird” flying against the flock.
How to Detect LLM Hallucinations with Displacement Consistency
We formalize this detection method as Displacement Consistency (DC). Instead of needing a source document or a second LLM at inference time, we measure how well a new displacement aligns with a reference set. I have seen similar issues when talking about Technical Debt in AI Development, where developers prioritize speed over rigorous validation.
To implement this, you need a small calibration set of grounded pairs. For a new query, you find its neighbors in the embedding space and calculate the mean direction. If the new answer deviates from this mean, it is likely a hallucination. Here is a simplified logic block using Python (which you would typically run as a microservice for your WordPress site):
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
def bbioon_calculate_dc_score(q_vec, a_vec, neighbor_qs, neighbor_as):
# Calculate the displacement of the current pair
v_current = a_vec - q_vec
# Calculate displacements for grounded neighbors
v_neighbors = neighbor_as - neighbor_qs
# Get the mean direction of the flock
v_mean_direction = np.mean(v_neighbors, axis=0)
# The DC Score is the cosine similarity (0 to 1)
# Higher = Grounded, Lower = Hallucination
score = cosine_similarity(
v_current.reshape(1, -1),
v_mean_direction.reshape(1, -1)
)
return float(score[0][0])The Catch: Domain Locality
While DC works remarkably well—often achieving near-perfect discrimination in benchmarks—it is not a universal “truth” detector. Grounding is a domain-local geometric property. As noted in recent research on arXiv, the “grounded direction” for legal advice is different from the direction for medical advice. Therefore, you must calibrate your reference set to your specific use case.
Look, if this detect LLM hallucinations stuff is eating up your dev hours, let me handle it. I’ve been wrestling with WordPress since the 4.x days.
Takeaway for WordPress Developers
Stop wasting tokens on “Judge LLMs” that can be fooled by the same biases as your generator. Instead, leverage the mathematical structure of your embeddings. By measuring cosine similarity between displacement vectors, you can build a more stable, performant, and reliable AI integration that actually knows when it is guessing.
