Overcoming LLM hallucinations in regulated industries: Artificial Genius’s deterministic models on Amazon Nova

2026-03-23 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Data Science & Analytics · Depth: Advanced, long

Summary

Artificial Genius, an AWS ISV Partner, has developed a third-generation language model solution to address the hallucination problem in large language models (LLMs) for highly regulated industries like financial services and healthcare. This approach, showcased using Amazon SageMaker AI and Amazon Nova, combines the contextual understanding of probabilistic models with a deterministic output layer. Unlike traditional generative AI, which struggles with auditability and accuracy due to its probabilistic nature, this patented method uses models strictly non-generatively. It achieves this by post-training Amazon Nova base models with instruction tuning to tilt log-probabilities towards absolute ones or zeros, effectively preventing the model from generating information not present in the input. This enables enterprise-grade adoption by ensuring accurate, relevant, and reproducible outcomes, improving upon Retrieval Augmented Generation (RAG) by creating unified embeddings for higher fidelity.

Key takeaway

For AI Engineers and Data Scientists building LLM solutions in regulated environments, this third-generation approach offers a blueprint for achieving deterministic, auditable outputs. You should consider adopting non-generative fine-tuning techniques on models like Amazon Nova within SageMaker AI to mitigate hallucinations, especially when developing applications for finance, healthcare, or legal sectors where accuracy and reproducibility are critical. Prioritize high-quality, diverse synthetic training data, including negative examples, to ensure model reliability and prevent overfitting.

Key insights

A hybrid AI architecture enables deterministic, non-hallucinating LLM outputs for regulated industries by using generative models non-generatively.

Principles

• Determinism and auditability are engineered features, not assumptions.
• Data engineering is paramount for specialized fine-tuning success.
• Constrain model capabilities for reliability in enterprise AI.

Method

Post-train a foundation model (e.g., Amazon Nova Lite) using Low-Rank Adaptation (LoRA) and a proprietary synthetic, non-generative Q&A dataset to enforce a "do not make up answers" instruction, achieving deterministic outputs.

In practice

• Use LoRA dropout (around 50%) for regularization during fine-tuning.
• Increase synthetic training data quantity and diversity to prevent overfitting.
• Employ prompt meta-injection to disable unwanted CoT behavior in models.

Topics

• LLM Hallucination Mitigation
• Non-Generative AI
• Amazon SageMaker
• Financial Services AI
• Instruction Tuning

Best for: AI Engineer, MLOps Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.