Artificial Genius addresses a critical challenge facing regulated industries by delivering deterministic models for applications on Amazon Web Services. The company’s third-generation language models offer a solution to the common problem of “hallucinations,” plausible but inaccurate outputs, that hinder the use of standard large language models in sectors like finance and healthcare where auditability is paramount. Unlike previous approaches, Artificial Genius leverages Amazon SageMaker AI and Amazon Nova in a hybrid architecture that bridges the gap between the rigidity of symbolic logic and the unpredictability of probabilistic models, ensuring accuracy and reproducibility. “For a bank or a hospital, determinism isn’t only a goal; the outcomes must be accurate, relevant, and reproducible,” the company states, moving beyond simply lowering the risk of errors to mathematically removing output probabilities. This innovation promises to unlock the potential of AI in highly sensitive areas without sacrificing factual correctness.
LLM Hallucinations & Determinism in Regulated Industries
Earlier AI relied on rigid, rule-based systems lacking adaptability, while more recent models prioritize fluency at the expense of truthfulness. A third generation is emerging that seeks to bridge this gap. Artificial Genius, an AWS ISV Partner, is developing a solution utilizing Amazon SageMaker AI and Amazon Nova, focusing on deterministic outputs rather than probabilistic ones. The company’s approach acknowledges the evolution of AI, moving from symbolic logic and probabilistic models toward a hybrid architecture. This isn’t about replacing existing models, but layering a deterministic process onto the generative power of Nova to ensure both contextual understanding and factual correctness. The core innovation lies in utilizing the model strictly non-generatively; the extensive probability information learned is applied to interpret input, rather than fabricate answers. This strategy addresses a fundamental mathematical challenge: preventing hallucinations within generative models.
Artificial Genius achieves this by post-training the model to prioritize absolute log-probabilities, essentially forcing it to respond with “zeros” or “ones” when lacking definitive information. “Don’t make up answers that don’t exist,” is the single system instruction guiding this process, creating a safety profile suitable for sensitive applications. This differs from simply lowering the “temperature” of a model, which often fails to fully resolve the hallucination problem. The third-generation approach improves upon Retrieval Augmented Generation (RAG) by embedding both input text and user queries into a unified embedding, ensuring greater relevance and fidelity than standard vector retrieval methods. The company packages this model into an industry-standard agentic client-server platform, available through AWS Marketplace, enabling complex automation with increased reliability. Unlike second-generation agents prone to compounding errors, this third-generation model maintains high fidelity throughout workflows, which follow the structure of a product requirements document (PRD).
Through this structure, domain experts, who might not be AI engineers, can formulate queries in natural language while maintaining strict control over the output. The product additionally offers free-form prompting of the workflow specification. For this purpose, the Amazon Nova Premier model, which is especially capable of translating free-form prompts into PRD format, is used, though it requires human review. The key to this system is defining queries that are inherently non-generative, extracting or verifying information rather than predicting the next token. For example, a request for a direct quote from a document to support a previous answer is considered a non-generative task, as demonstrated by the example: “[{“role”: “user”, “content”: [{“text”: “Document: Financial performance remained strong…Question: Provide a quote…Answer:”}], }, {“role”: “assistant”, “content: [{“text”: ‘”Our revenue grew by 15% year-over-year…”}], } ]”.
Artificial Genius’s Third-Generation Hybrid Architecture
The current pursuit of artificial intelligence within heavily regulated sectors presents a paradox; while large language models offer powerful analytical capabilities, their propensity for “hallucinations,” generating factually incorrect information, hinders adoption in areas demanding absolute accuracy. First-generation AI relied on deterministic, rule-based systems, safe but limited in scope, while second-generation models, powered by the Transformer architecture, achieved impressive fluency at the cost of predictability and scalability. This inherent probabilistic nature creates challenges for industries like finance and healthcare where auditability and reproducibility are paramount, as outcomes must be not only accurate but demonstrably so. Artificial Genius is addressing these limitations with a third-generation hybrid architecture, moving beyond the constraints of purely symbolic or probabilistic approaches. Rather than replacing existing technologies, this innovation bridges the gap between the fluency of generative AI and the reliability of deterministic systems.
The company leverages the generative power of Amazon Nova to understand context, then applies a deterministic layer to verify and produce output, achieving what they describe as “the convergence of fluency and factuality.” This approach fundamentally alters how language models are utilized, shifting from prediction to extraction and verification. A core element of this architecture is the use of the model strictly non-generatively, meaning it doesn’t predict the next token but instead relies on interpolation of existing information within the input. “It’s mathematically difficult to prevent standard generative models from hallucinating because the extrapolative, generative process itself causes errors,” explain Paul Burchard and Igor Halperin of Artificial Genius. To achieve this, the company employs a patented instruction tuning method within Amazon SageMaker AI, post-training the Amazon Nova base models to prioritize absolute log-probabilities, effectively removing output probabilities. The system can translate free-form prompts into PRD format using Amazon Nova Premier, though this generative step includes a human checkpoint to ensure accuracy.
For enterprise AI, the primary objective is often to intelligently constrain a model’s vast capabilities to help ensure reliability, rather than unleashing its full generative potential.
Non-Generative Instruction Tuning with SageMaker AI
Artificial Genius is tackling a core challenge in deploying large language models (LLMs) within heavily regulated sectors, specifically focusing on eliminating the risk of “hallucinations,” factually incorrect statements presented as truth. While LLMs offer considerable potential for analytics and compliance, their probabilistic nature has historically hindered adoption in industries demanding absolute accuracy and reproducibility; for institutions like banks and hospitals, determinism isn’t merely desirable, it’s essential. The company is leveraging Amazon SageMaker AI and Amazon Nova to deliver a solution that is probabilistic on input but deterministic on output, a feat achieved through a novel approach to instruction tuning. The evolution of AI has seen a shift from first generation (1950s) models, which used symbolic logic to build deterministic, rule-based models that lacked fluency and could not scale, to the fluent, yet unreliable, probabilistic models dominating the present day.
This allows for comprehension of varied phrasing without relying on probabilistic generation, a departure from simply lowering a model’s “temperature,” which often proves insufficient. Our revenue grew by 15% year-over-year… Question: What was the annual revenue growth? Answer:”}], }, { “role”: “assistant”, “content”: [{“text”: “15%”}] } ]”.
By prioritizing engineered trust over unconstrained generation, this approach paves the way for the responsible and impactful adoption of AI in the world’s most critical sectors.
Beyond RAG: Unified Embeddings for Enhanced Relevance
The pursuit of accurate information retrieval from large language models (LLMs) has moved beyond simply augmenting models with external knowledge via Retrieval Augmented Generation (RAG). This advancement addresses a key limitation of RAG, which, while helpful, remains a generative process susceptible to inaccuracies and relevance drift as queries evolve. Traditional vector retrieval methods used in RAG create fixed embeddings, potentially missing nuanced connections in subsequent interactions; the third-generation approach improves upon RAG by effectively embedding the input text and the user query into a unified embedding. This helps ensure that the data processing is inherently relevant to the specific question asked, delivering higher fidelity and relevance. This isn’t merely about finding something related, but about pinpointing the most relevant information with greater consistency. The company achieves this by shifting away from probabilistic generation toward a deterministic output, a critical step for industries where auditability and accuracy are paramount.
The core of this innovation involves a paradoxical approach: leveraging the power of generative models like Amazon Nova, but utilizing them strictly non-generatively. This means the model doesn’t predict the answer, but rather extracts or verifies information solely from the provided context. This capability is built upon a foundation of instruction tuning performed on Amazon Nova base models using SageMaker AI, a patented method that effectively removes output probabilities. The resulting system isn’t simply about avoiding hallucinations; it’s about creating a mathematical loophole where the model retains its understanding of data while operating with a safety profile suitable for highly regulated sectors like finance and healthcare.
Data engineering is paramount : The success of highly specialized fine-tuning is overwhelmingly dependent on the quality and intelligent design of the training data to prevent overfitting.
Agentic Workflows & PRD-Structured Prompting
The prevailing assumption that artificial intelligence demands constant human oversight is being challenged by a shift toward more reliable, deterministic workflows. While early AI systems relied on rigid, rule-based logic, and contemporary models often exhibit unpredictable “hallucinations,” a new approach from AWS ISV Partner Artificial Genius aims to bridge the gap between them, delivering fluency with factual accuracy. Artificial Genius addresses the fundamental problem of AI-generated inaccuracies by utilizing the model strictly non-generatively, a strategy that moves beyond simply attempting to control randomness. Their solution focuses on interpolation, understanding the nuances of input, rather than relying on probability to create an answer. Unlike traditional agents that can compound errors, this third-generation model’s inherent reliability enables complex automation. The company demonstrates this approach with examples of JSON interactions, showing how the system handles answerable and unanswerable questions, even providing an “Unknown” response when information is absent. “While short answers (such as dates or names) are obviously non-generative, it’s also possible to output long sequences deterministically,” the company notes, illustrating with an example of extracting a supporting quote from a document.
The hallucination rate is unambiguous and straightforward to calculate, it’s the percentage of unanswerable questions that were answered with anything except the instructed non-answer.
