AI Decision Models: A Comparison with LLMs

Decision models have recently emerged as a distinct paradigm in the field of artificial intelligence. A prominent example often discussed is Jev, developed by TypeSafe AI. Unlike generative models, Jev generates structured selections, numerical scores, and confidence metrics rather than free-form text.

Although conventional AI systems have long excelled at prediction and classification, Jev is engineered to deliver structured decisions that software systems can integrate directly. This methodology provides an alternative framework for developing AI applications, particularly in scenarios where a system must evaluate incoming data and determine subsequent actions.

How does Jev work?

In a traditional Large Language Model (LLM) workflow, you submit a prompt and receive generated text as the output. Even when the request specifies JSON, the application must still parse and validate the response before it can be utilized.

Jev operates on a different logic. You supply the necessary context, define the specific questions to be addressed, and specify the expected data type for the answers. Jev then returns structured results that your application can process without additional parsing steps.

Jev supports three primary question types:

• Choice: Selects a specific 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 priority level of low, medium, or high.
• Yes or no: Estimates the probability of a statement’s truth, such as determining if a support ticket requires human intervention.

Multiple questions regarding the same input can be addressed within a single request. For instance, a support application could simultaneously classify a ticket, assess its urgency, and determine if it needs human review. Jev returns these results in a structured format, enabling the application to immediately determine the next course of action.

Decision models vs. LLMs

LLMs and decision models serve distinct functions. While an LLM is capable of explaining decisions, generating content, writing code, and performing complex reasoning, Jev is specifically designed for focused decision-making tasks with defined outputs.

The distinction does not imply that LLMs are incapable of making decisions; they can certainly classify information and return structured data. However, Jev is purpose-built for this specific task, eliminating the need to generate and interpret free-form responses.

This makes Jev particularly effective for repeated, well-defined decision processes. Conversely, for tasks requiring 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 assessing the strength of the model’s preference for a particular answer.

For example, a customer support application might query whether a ticket should be escalated. If the model indicates a high probability of escalation, the application could forward the ticket to a senior support team. If the result is uncertain, the system could route it for human review.

It is crucial to note that confidence does not guarantee accuracy. A model may be highly confident in an incorrect 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 an answer and a separate confidence score. The precise meaning depends on the output type. Applications should 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 sorting incoming information before processing. A support platform might categorize requests by topic to direct them to the appropriate team. Similarly, a business application could categorize documents, emails, or customer feedback.

Jev can return a category from a predefined list, allowing the application to route the item without the need to parse generated paragraphs.

Scoring and evaluation

Decision models can also evaluate information against a defined scale. For instance, an application could rate a support ticket by urgency, score a document for relevance, or assess whether a response meets specific criteria.

These results can be utilized to rank items, prioritize workflows, or identify cases requiring further review. The quality of these decisions ultimately depends on the model 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 tool, classify a request, or determine if a task requires additional information.

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

For example, a custom workflow using Hermes Agent could utilize 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 approach requires a custom integration rather than relying on assumed native support.

Decision models should not be relied upon as standalone security controls. If an agent is capable of executing commands or modifying files, permissions and deterministic rules must strictly enforce which actions are permitted. A model's classification can inform this process but should not replace strict access controls.

Alternatives to Jev

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

OpenJev

OpenJev is an open project aligned with the Jev approach. It is worth examining if you prefer an implementation outside of a hosted service. Before deployment, verify its current availability, hardware requirements, supported features, and licensing terms.

Laya

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

These projects should be evaluated based on actual performance rather than just their stated capabilities. Use consistent test data to measure accuracy, latency, resource requirements, and the alignment of their confidence estimates with real outcomes.

Can you run decision models locally?

Running a model locally provides greater control over the software environment and data processing location. However, deployment options vary by project. Jev is accessible via an API, while open-source alternatives may offer the option to run inference on your own hardware.

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

If you wish to experiment with local AI models, DaDesktop offers cloud desktops with dedicated GPU options. These can be used for development and testing without the need to purchase high-end local hardware.

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

When should you use a decision model?

Decision models are beneficial when an application requires a defined answer that triggers a specific next step. They complement LLMs by handling classification, scoring, and routing, while generative models manage language processing and more complex reasoning.

The optimal choice depends on your specific application. Test the model against your own data, compare it with conventional classifiers and LLMs, and measure accuracy, latency, and cost. For critical decisions, incorporate human review and safeguards where necessary.

Want to discuss your use case? Contact us and we'll guide you in the right direction.