AI Decision Models: How They Differ from LLMs

Decision models have recently emerged as a distinct approach to AI, with Jev from TypeSafe AI being one of the most discussed examples. Unlike traditional models, Jev generates structured choices, scores, and confidence estimates rather than raw text.

Although conventional AI models have long engaged in predictions and classifications, Jev specializes in producing structured decisions that software can utilize directly. This method provides an alternative framework for developing AI applications, particularly in scenarios where systems must assess information and determine the next course of action.

How does Jev work?

In a standard LLM workflow, you submit a prompt and receive generated text. Even when JSON output is requested, the application must still process and validate the response content before it can be used.

Jev operates on a different principle. You supply context, define the specific questions to be answered, and specify the expected answer type. In return, Jev delivers structured results that your application can process immediately.

Jev supports three primary question types:

• Choice: Selects an option from a predefined list, such as billing, technical support, or sales.
• Score: Evaluates input against an ordered scale, such as low, medium, or high priority.
• Yes or no: Estimates the probability of a statement’s truthfulness, such as determining if a support ticket requires human attention.

Multiple questions regarding the same input can be asked in a single request. For instance, a support application might classify a ticket, assess its urgency, and determine if human review is necessary. Jev returns these results in a structured format, enabling the application to decide on the subsequent action.

Decision models vs. LLMs

LLMs and decision models serve distinct purposes. While an LLM can explain decisions, generate content, write code, or engage in complex reasoning, Jev is engineered for focused decisions with clearly defined outputs.

This distinction does not imply that LLMs are incapable of making decisions; they can classify information and return structured data. However, Jev is specifically designed for this task, eliminating the need for applications to interpret free-form responses.

This makes Jev particularly useful for repetitive, well-defined decisions. 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 that its outputs include probabilities and confidence metrics. These values assist developers in gauging how strongly the model favors a particular answer.

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

However, confidence does not equate to correctness. A model can be highly confident yet still make an incorrect decision. Developers should validate results against real-world examples and establish thresholds that align with the potential consequences of each decision.

It is also crucial to differentiate between the probability of an answer and a separate confidence score. The specific meaning varies by output type. Applications should adhere to the model's documentation rather than assuming every returned number represents the same metric.

Where can decision models be useful?

Classification and routing

Many applications require the sorting of incoming information prior to processing. A support platform, for instance, might categorize requests by topic and 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, enabling the application to route items without the need to parse generated paragraphs.

Scoring and evaluation

Decision models are also capable of evaluating information against defined scales. For instance, an application could rate a support ticket’s urgency, score a document’s relevance, or assess whether a response meets specific criteria.

These results can be utilized to rank items, prioritize workloads, or identify cases requiring further review. The quality of such decisions relies on both the model and how accurately the criteria describe the task.

AI agents and tool selection

AI agents utilize 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 general-purpose LLMs can make these decisions as part of their reasoning process, a decision model can handle more narrow choices prior to the agent’s action.

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 typically requires custom integration rather than relying on assumed native support.

Decision models should not be viewed as standalone security controls. If an agent can execute commands or modify files, permissions and deterministic rules must enforce which actions are permitted. A model’s classification can inform this process but should not replace it.

Alternatives to Jev

Jev is part of a broader category of models designed to generate structured decisions. OpenJev and Laya are two other projects worth exploring when comparing different approaches.

OpenJev

OpenJev is an open project associated with the Jev approach. It is worth investigating if you wish to explore implementations outside of hosted services. Before adoption, verify its current availability, hardware requirements, supported features, and license.

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. It may be relevant if you intend to evaluate a model that can run in your own environment.

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

Can you run decision models locally?

Running models locally offers greater control over the software environment and data processing location. However, deployment options vary by project. Jev is provided via an API, whereas open-source alternatives may offer options for running 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 provides cloud desktops with dedicated GPU options. These can be used for development and testing without the need to purchase high-end GPUs for your local computer.

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 and 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 point you in the right direction.