AI Decision Models: How They Differ from LLMs

In the AI landscape, decision models have recently emerged as a distinct and increasingly popular alternative approach. One notable example is Jev, developed by TypeSafe AI. Unlike generative models, Jev is designed to yield structured choices, scores, and confidence estimates rather than generating natural language text.

Although traditional AI models have long been used for prediction and classification, Jev shifts the focus toward making structured decisions that software can utilize immediately. This approach provides a robust alternative for building AI applications, particularly in scenarios where a system must evaluate incoming data and determine the appropriate next action without ambiguity.

How does Jev work?

When interacting with a traditional Large Language Model (LLM), the workflow typically involves submitting a prompt and receiving generated text as output. Even if the request specifies a structured format like JSON, the application is generally required to parse, handle, and validate the response before it can be safely used.

Jev operates on a different principle. You supply the necessary context, define specific questions to be answered, and specify the expected type of answer. In return, Jev delivers structured results that your application can process directly, eliminating the need for complex post-processing of free-form text.

Jev supports three primary categories of questions:

• Choice: Selects an option from a predefined list, such as categorizing a request as billing, technical support, or sales.
• Score: Rates an item against an ordered scale, such as assigning a low, medium, or high priority.
• Yes or no: Estimates the probability that a statement is true, such as determining whether a support ticket requires human intervention.

It is possible to combine multiple questions about the same input within a single request. For instance, a support application could simultaneously classify a ticket, assess its urgency, and evaluate the need for human review. Jev returns these results in a consistent, structured format, enabling the application to seamlessly execute the next step.

Decision models vs. LLMs

LLMs and decision models serve complementary but distinct roles. An LLM is versatile, capable of explaining decisions, generating creative content, writing code, or reasoning through complex, multi-step tasks. Jev, on the other hand, is engineered for focused decision-making with strictly defined outputs.

This distinction does not imply that LLMs are incapable of making decisions. They can certainly classify information and return structured data. However, Jev is specifically optimized for this task, avoiding the generation of free-form responses that require interpretation by the application.

This makes decision models particularly effective for repeated, well-defined decisions. Conversely, for tasks demanding detailed explanations, creative output, or complex multi-step reasoning, an LLM remains the more appropriate tool.

What are confidence scores?

A key feature of Jev is the inclusion of probabilities and confidence metrics in its outputs. These values assist developers in gauging how strongly the model favors a particular answer.

Consider a customer support application asking whether a ticket should be escalated. If the model indicates a high probability that escalation is necessary, the application can automatically route the ticket to a senior support team. Conversely, if the result is uncertain, the system can flag the ticket for human review.

However, it is crucial to note that confidence does not equate to correctness. A model may express high confidence while still making an erroneous decision. Developers are advised to test results against real-world examples and establish thresholds that align with the potential consequences of each decision.

Furthermore, it is important to distinguish between the probability of a specific answer and a separate confidence score. The precise meaning can vary depending on the output type. Applications should strictly adhere to the model's documentation rather than assuming all returned numerical values represent the same measurement.

Where can decision models be useful?

Classification and routing

Many applications require the organization of incoming data before further processing. A support platform might sort requests by topic to direct them to the appropriate team, while a business application could categorize documents, emails, or customer feedback.

Jev can return a category from a predefined list, enabling the application to route items efficiently without needing to parse a generated paragraph.

Scoring and evaluation

Decision models are also effective for evaluating information against a defined scale. For example, an application could rate the urgency of a support ticket, score a document for relevance, or assess whether a response meets specific quality criteria.

These results can be utilized to rank items, prioritize workflows, or identify cases requiring deeper analysis. The quality of these decisions remains dependent on the model’s performance and how accurately the criteria describe the task.

AI agents and tool selection

AI agents rely on models to determine which actions to execute. An agent might select a specific tool, classify a request, or decide whether a task requires additional information.

While a general-purpose LLM can handle these decisions as part of its broader reasoning process, a decision model can manage narrower, specific choices before the agent proceeds.

For example, a custom workflow using Hermes Agent could employ Jev to classify an incoming task before passing it to the agent. It could also assist in determining which workflow should handle a specific request. This would typically require a custom integration rather than relying on assumed native support.

It is essential not to treat decision models as standalone security controls. If an agent has the capability to execute commands or modify files, permissions and deterministic rules must enforce which actions are allowed. While a model’s classification can inform this process, it should not serve as a substitute for rigorous security measures.

Alternatives to Jev

Jev represents one part of a broader category of models designed to produce structured decisions. When comparing approaches, OpenJev and Laya are two other projects worth exploring.

OpenJev

OpenJev is an open project aligned with the Jev approach. It may be worth investigating if you prefer an implementation outside of a hosted service. Before adopting it, ensure to verify its current availability, hardware requirements, supported features, and licensing terms.

Laya

Laya is an open-source decision model developed by Convai Innovations. It focuses on tasks such as selecting from predefined options, rating information, and answering yes-or-no questions. This model may be relevant if you intend to evaluate a solution that can be deployed in your own environment.

When comparing these projects, focus on actual performance rather than just stated capabilities. Use identical test data to measure accuracy, latency, resource requirements, and how closely their confidence estimates align with real-world outcomes.

Can you run decision models locally?

Deploying a model locally offers greater control over the software environment and data processing locations. However, deployment options vary significantly by project. While Jev is offered through an API, open-source alternatives may provide flexibility for running inference on your own hardware.

Hardware requirements are influenced by model size, memory usage, and throughput. Some models can operate effectively on a CPU, while others benefit from GPU acceleration, particularly when handling high volumes of requests.

For those looking to experiment with local AI models, DaDesktop offers cloud desktops equipped with dedicated GPU options. This allows for development and testing without the need to purchase high-end GPU hardware for local machines.

Learn more about running local LLMs or explore AI agent environments.

When should you use a decision model?

Decision models are most useful when an application requires a defined answer that can trigger a specific next step. They complement LLMs by handling classification, scoring, and routing, while allowing generative models to focus on language generation and more complex reasoning.

The optimal choice ultimately depends on your specific application. It is recommended to test the model against your own data, compare it with conventional classifiers and LLMs, and measure key metrics such as accuracy, latency, and cost. For critical decisions, always incorporate human review and appropriate safeguards.

Want to discuss your use case? Contact us, and we will help guide you in the right direction.