While guardrails in prompts are useful, a more effective step to prevent AI agents from hallucinating is careful model selection. For instance, using Google's Gemini models, which are noted to hallucinate less, provides a stronger foundational safety layer than relying solely on prompt engineering with more 'creative' models.

Billions have been invested in the LLM data center and hardware ecosystem, creating a powerful inertia. For an alternative architecture like EBMs to succeed, it cannot demand a full replacement. Instead, it must position itself as a compatible layer that makes existing LLM investments cheaper and more effective for specific tasks like spatial reasoning.

To solve for AI hallucinations in high-stakes decisions, advanced platforms use the LLM as an interpreter that writes code to query raw data. If data is unavailable, it returns an error instead of fabricating an answer, making every analysis fully auditable and grounded in verifiable data.

LLMs' intelligence is dependent on the language they are trained on, meaning their reasoning process differs between, for example, English and French. This is unnatural for tasks like spatial reasoning, which are language-agnostic. EBMs operate on an abstract, token-free level, mapping information directly without a language-based intermediary.

LLMs operate autoregressively, making one decision (token) at a time without seeing the full problem space. This can lead to hallucinations or dead ends. EBMs are non-autoregressive, allowing them to see all possible routes simultaneously and select an optimal path, much like having a bird's-eye view of a map to avoid a hole in the road.

Instead of relying solely on 'black box' LLMs, a more robust approach is neurosymbolic computation. This method combines three estimators: a traditional symbolic/rule-based model (e.g., a medical checklist), a neural network prediction, and an LLM's assessment. By comparing these diverse outputs, experts can make more informed and reliable judgments.

EBMs are based on a fundamental principle in physics where systems naturally seek their lowest energy state (e.g., sitting on a couch when tired). The model maps all possible outcomes onto an 'energy landscape,' where the lowest points represent the most probable solutions. This avoids the expensive, token-by-token guessing game played by LLMs.

To ensure scientific validity and mitigate the risk of AI hallucinations, a hybrid approach is most effective. By combining AI's pattern-matching capabilities with traditional physics-based simulation methods, researchers can create a feedback loop where one system validates the other, increasing confidence in the final results.

To deploy LLMs in high-stakes environments like finance, combine them with deterministic checks. For example, use a traditional algorithm to calculate cash flow and only surface the LLM's answer if it falls within an acceptable range. This prevents hallucinations and ensures reliability.