Navigating the Paradox of Large Language Models in Regulated Industries

Addressing Hallucinations for Safe AI Adoption in Financial and Healthcare Sectors

The Evolution of Language Models: From Deterministic to Probabilistic to Non-Generative

The Solution: A Paradoxical Approach to Generation

Going Beyond Retrieval Augmented Generation (RAG)

Maximizing Value with Agentic Workflows

Implementing Non-Generative Query Strategies

Designing an AWS Reference Architecture

Step-by-Step Guide for Non-Generative Fine-Tuning

A Quantitative Analysis of Performance and Verifiability

Key Insights for Trustworthy AI in Regulated Environments

Conclusion: The Future of Reliable AI in Finance and Healthcare

About the Authors

Feel free to modify any of the headings as needed!

# The Paradox of Language Models in Regulated Industries: A New Path Forward

*This post is cowritten by Paul Burchard and Igor Halperin from Artificial Genius.*

The rise of large language models (LLMs) presents a striking paradox for highly regulated industries such as financial services and healthcare. While these models have demonstrated an extraordinary ability to process complex, unstructured information, offering transformative potential for analytics, compliance, and risk management, they also manifest a significant risk: their inherent probabilistic nature frequently leads to “hallucinations,” or plausible but factually incorrect responses.

In sectors governed by stringent requirements for auditability and accuracy, the non-deterministic behavior of traditional generative AI poses a critical barrier to adoption in mission-critical applications. For organizations like banks and hospitals, determinism is not merely desirable; it is essential. The outputs generated must be accurate, relevant, and reproducible.

In this post, we’re excited to showcase how AWS ISV Partner Artificial Genius is addressing this challenge through the use of Amazon SageMaker AI and Amazon Nova. By introducing a new generation of language models, they offer a solution that is probabilistic on input but deterministic on output, thereby facilitating the safe, enterprise-grade adoption of LLMs.

## The Evolution of AI

To appreciate how we arrived at this solution, let’s dive into the evolution of AI:

– **First Generation (1950s):** Researchers developed deterministic, rule-based models using symbolic logic. While these models ensured safety, they lacked fluency and scalability.
– **Second Generation (1980s-Present):** The shift toward probabilistic models (culminating in the Transformer architecture) unlocked unprecedented fluency, yet these models suffered from unbounded failure modes (hallucinations) that are hard to eliminate.
– **Third Generation (Artificial Genius Approach):** Instead of replacing previous generations, we’re innovating towards a hybrid architecture. This combines the generative power of Amazon Nova for contextual understanding while implementing a deterministic layer to verify and produce output—representing a convergence of fluency and factual accuracy.

## The Solution: A Paradoxical Approach to Generation

Mitigating hallucinations in standard generative models is mathematically challenging due to their extrapolative processes. Artificial Genius tackles this by employing a strictly non-generative model. The vast probability information captured during training is utilized only to interpolate inputs, allowing comprehension of different ways information or inquiry can be expressed without relying on probability to produce answers.

By fine-tuning Amazon Nova base models through SageMaker AI, Artificial Genius has patented a method that effectively removes output probabilities. Unlike traditional models that reduce temperature to zero to enforce determinism (often ineffectively), our approach adjusts log-probabilities of next-token predictions toward absolute values of either one or zero. This post-training paradigm enforces a strict instruction: do not fabricate answers.

This innovates a method where the model retains its advanced understanding of data while adhering to the safety profiles required for finance and healthcare.

## Beyond Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation (RAG) is often touted as a solution for accuracy, yet it remains inherently generative and suffers from fixed vector embeddings that may not always be relevant for future queries. Our third-generation approach surpasses RAG by embedding both the input text and the user query into a unified embedding. This paradigm ensures that data processing is directly pertinent to the specific inquiry, yielding higher fidelity and contextual relevance than conventional retrieval methods.

## Delivering Value with Agentic Workflows

To help enterprises fully exploit their unstructured data, Artificial Genius packages this model into a client-server platform available through the AWS Marketplace. Unlike second-generation agents that may propagate errors through interconnected workflows, our third-generation model is built on inherent reliability, enabling complex, high-fidelity automation.

Prompts are structured like a product requirements document (PRD), allowing domain experts—often without AI engineering backgrounds—to craft queries in natural language while maintaining firm control over outputs. Our platform additionally supports free-form prompting using the Amazon Nova Premier model, adept at translating free-form queries into PRD formats.

## Defining the Non-Generative Query

The crux of our model is defining generative tasks in a strictly non-generative manner. Responses to questions—whether short answers or complex follow-ups—do not rely on probabilistic token prediction but rather on the verification of existing information based on input context.

For example, consider the following structured interactions:

**1. Short Answer:**
“`json
[
{
“role”: “user”,
“content”: [{“text”: “Document: Financial performance remained strong through the third quarter. Our revenue grew by 15% year-over-year. Question: What was the annual revenue growth? Answer:”}]
},
{
“role”: “assistant”,
“content”: [{“text”: “15%”}]
}
]

2. Long-Answer Follow-Up:

[

  {

    "role": "user",

    "content": [{"text": "Document: Financial performance remained strong through the third quarter. Our revenue grew by 15% year-over-year. Question: Provide a quote from the document showing that the annual revenue growth was 15%. Answer:"}]

  },

  {

    "role": "assistant",

    "content": [{"text": "\"Our revenue grew by 15% year-over-year.\""}]

  }

]

3. Unanswerable Question:
[

  {

    "role": "user",

    "content": [{"text": "Document: Financial performance remained strong through the third quarter. Our revenue grew by 15% year-over-year. Question: What was the CEO’s bonus this year? Answer:"}]

  },

  {

    "role": "assistant",

    "content": [{"text": "Unknown"}]

  }

]

These examples illustrate our non-generative query architecture and will accompany the rollout of third-generation language model products to guide users in constructing effective queries.
AWS Reference Architecture
Our streamlined architecture customizes foundation models effectively and is characterized by:

Data Storage: Training data (synthetic Q&A) is stored in Amazon S3.
Training: SageMaker Training jobs provision necessary compute resources for fine-tuning the Nova model.
Deployment: Fine-tuned models are integrated into Amazon Bedrock for secure and scalable inference.

This design maintains clear data lineage—which is essential for audit trails in financial services.
Technical Implementation: Step-by-Step Guide
Creating a third-generation language model involves several essential steps:


Select the Base Model: Using Amazon Nova family models, specifically the Nova Lite model, which is inclined to produce concise outputs without being verbose.


Post-Training Instruction: Post-training fine-tune the model to ensure compliance with a core principle: if a question cannot be answered directly from the document, simply respond with "Unknown."


High-Quality Training Data: Generating synthetic, non-generative Q&A scenarios that provide variety and complexity to effectively train the model.


Fine-Tuning: Employ Low-Rank Adaptation (LoRA) for post-training that preserves the foundation model's comprehension while preventing overfitting through methods like regularization and diverse synthetic data generation.


Manual Oversight: Ensure validation through checkpoints based on tracking validation metrics to determine optimal stopping points during training.


Empirical Testing: Quantitative evaluations reveal effective performance, including a hallucination rate of 0.03%.


Lessons Learned and Insights


Data Engineering is Key: High-quality, intelligently designed training data is essential to prevent overfitting and hallucinations.


Capability vs. Control: For enterprise applications, balancing expansive model capabilities with controlled outputs is crucial for reliability.


Iterative Development: Continuous validation and flexibility in the development process enable effective refinement of models.


Conclusion: The Future of Trustworthy AI in Finance
The methodologies outlined in this narrative present a practical framework for building deterministic, non-hallucinating LLMs tailored for critical enterprise roles. By leveraging non-generative fine-tuning on foundation models like Amazon Nova via SageMaker Training Jobs, organizations can create AI systems adhering to stringent standards of accuracy, auditability, and reliability.
This approach establishes a transferable blueprint for any regulated industry—be it legal, healthcare, or insurance—where AI-driven insights must be accurate and traceable. Moving forward, the goal is to scale this methodology across diverse use cases while exploring model distillation techniques for creating optimized worker models.
By prioritizing engineered trust over unfettered generation, we’re paving the way for responsible and impactful AI adoption in our most essential sectors.
About the Authors
Paul Burchard: Founder and Partner of Artificial Genius, Paul has an extensive background in finance and AI innovation, specializing in data privacy and artificial intelligence systems.
Igor Halperin: Vice President in the GenAI group at Fidelity Investments, Igor is noted for his expertise in financial machine learning and has significant academic contributions in finance.
Contributions from industry experts and specialists have guided the development of these methodologies. As we advance, we remain committed to fostering responsible AI integration in every critical sector.

This blog post tackles the paradox of using language models in regulated industries, highlighting innovative solutions while providing technical insights and actionable steps for implementation.