In the rapidly evolving landscape of artificial intelligence, large language models have become increasingly sophisticated. However, these systems face a fundamental challenge: they operate by predicting the next word in a sequence, which can sometimes lead them to "go rogue" – generating responses that drift from factual accuracy, established guidelines, or intended messaging.

This article explores how SimScore, a consensus analysis tool for written feedback, offers a promising solution to this critical limitation of AI systems.

The Next-Word Prediction Problem

Modern AI systems like ChatGPT, Claude, and others rely on a surprisingly simple core mechanism: predicting what word should come next in a sequence. While remarkably effective, this approach creates inherent vulnerabilities:

• Hallucinations: AI systems can confidently state incorrect information because the sequence of words appears statistically plausible
• Consistency issues: Responses may contradict themselves as the model focuses on local coherence rather than global consistency
• Wandering off-topic: Without guardrails, responses can drift into unrelated territory
• Loss of context: Important details from earlier in a conversation may be forgotten or misapplied

When clients experience these issues, it can rapidly erode trust and create negative perceptions about AI technology as a whole. For organizations implementing AI solutions, managing these risks is paramount.

Enter SimScore: A Data-Driven Guardrail

SimScore offers a sophisticated approach to addressing the limitations of next-word prediction models. Originally designed as a consensus analysis tool for written feedback, SimScore's methodology provides a powerful framework for keeping AI outputs aligned with desired parameters.

How SimScore Works

At its core, SimScore uses several key components:

1. Vector Transformation: Converts text responses into numerical vectors
2. Centroid Calculation: Determines a mathematical "consensus point" representing approved content
3. Similarity Scoring: Measures how closely an AI response aligns with the consensus point
4. Relationship Mapping: Visualizes the relationship between responses and the consensus

This approach allows organizations to establish clear parameters for AI outputs and ensure responses remain within acceptable bounds.

Implementing SimScore as an AI Guardrail