Hallucinations are one of the most familiar problems with generative AI.
Ask a chatbot an obscure question and it may confidently give you an answer that sounds completely reasonable, but may be entirely wrong.
For casual use, it’s pretty frustrating. But for training, it can be a much bigger problem.
I talked a bit about this on my blog last week and wanted to expand on what it means for training.
If an AI system is responsible for deciding how a simulated person behaves, whether a learner handled an interaction correctly, or what should happen next, a plausible mistake can directly change the lesson being taught, or the outcomes being measured.
That’s not a risk you should be willing to take in your business.
SkillSaige approaches that problem differently so you don’t have to worry about the problem.
Why Language Models Hallucinate
Large language models are exceptionally good at predicting language. But that is absolutely not the same thing as understanding the language, let alone the real-world ideas that language represents.
It sounds pedantic, but it is an incredibly important distinction when it comes to this topic.
An LLM learns enormous numbers of relationships between words, representations of concepts, and other pieces of language. Those relationships allow it to generate a convincing answer that fits the context of a question. That is, literally, its purpose.
Most of the time, that produces something useful (language is great that way), but not always.
When the model has strong information about a subject, the distinction may hardly matter. Close enough is still right in that case. But when information becomes weaker, the model can still have an excellent understanding of what the answer should sound like.
Which is how we end up with answers that are beautifully written, highly specific, and completely and totally wrong.
SkillSaige Changes the Architecture
A common response to hallucinations is to place more controls around the language model.
Give it better prompts. Connect it to a database. Tell it to verify its answer.
Those techniques can improve reliability by inches, but they still leave the LLM at the center of the decision-making process. And you can try to reduce the number of effectively infinite (and infinitely wrong) outputs by chunks, but it’s a game of diminishing returns (and exponential costs).
SkillSaige’s Logic Lattice takes a different approach.
During a roleplaying scenario, the language model handles language. The Logic Lattice handles the simulation.
The system determines the character’s objectives, behavioral state, possible reactions, and how the learner’s choices affect the interaction. It handles deterministic, quantitative evaluation of performance in a way that’s repeatable and diagnosable if you don’t see the measurements you expect.
Those relationships are explicit rather than generated on the fly by an LLM.
That means the model cannot suddenly decide that a terrible response was brilliant because it found a linguistically plausible way to justify it.
Deterministic Logic Creates Reliable Outcomes
Suppose a learner is practicing a difficult workplace conversation. They might phrase a good response in dozens of ways, right?
That flexibility is where an LLM is valuable. It can help interpret the learner’s natural language without requiring them to choose from a small list of predefined responses, or crossing your fingers and hoping you made a big enough parser table.
But once the meaning of that response has been understood, conventional logic can take over.
Did the learner acknowledge the concern?
Did they make the situation better or worse?
Did they cross a boundary that should fundamentally change the interaction?
Those decisions do not need to be invented every time someone runs a scenario. The system can evaluate them against the underlying structure of the simulation.
The LLM handles ambiguity. The Logic Lattice handles certainty.
Better AI Doesn’t Mean More AI
Generative AI is an extraordinary technology, but that does not mean it should be responsible for every part of an AI-powered product. I would argue (and often do) that it should only be a very small part of most. It’s a missing link in computational engineering, not the silver bullet to solve all your process woes.
Since we deliberately limit what the language model is allowed to control in SkillSaige, we are able to have natural, open-ended conversations without relying on a language model to determine the underlying truth of the simulation. It is, quite literally, not a chatbot. The AI never processes a full transcript of your conversation up until that point.
For training, that separation is incredibly important. A learner should be free to experiment without punishment.
But the system evaluating that experiment should not be making things up when it comes time to evaluate their progress.
At SkillSaige, we firmly believe that you should not have to choose between effective AI and reliability, predictability, or affordability. With our LMS, your knowledge and experience can be shared reliably, effectively, and transparently with your team.
