AI Decision Models: Distinguishing Them from LLMs

Decision models have recently drawn attention as a distinct approach within AI. A notable example is Jev, developed by TypeSafe AI. Instead of generating text, Jev outputs structured choices, scores, and confidence estimates.

Although traditional AI models have long handled predictions and classifications, Jev is specifically designed to produce structured decisions that software can utilise directly. This methodology provides an alternative path for building AI applications, particularly when a system must evaluate information to determine the next course of action.

How does Jev function?

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

Jev operates differently. You supply context, define the questions requiring answers, and specify the expected answer format. Jev then returns structured results that your application can process immediately.

Jev supports three primary question types:

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

You can pose multiple questions about the same input in a single request. For instance, a support application could classify a ticket, rate its urgency, and estimate whether a human should review it. Jev returns the results in a structured format, enabling the application to determine the next steps.

Decision models vs. LLMs

LLMs and decision models serve distinct purposes. An LLM can explain decisions, generate content, write code, or reason through complex tasks. Jev is engineered for focused decisions with defined outputs.

This distinction is not that LLMs cannot make decisions; they can certainly classify information and return structured data. However, Jev is built specifically around this task, eliminating the need to interpret free-form responses.

This makes it particularly useful for repeated, well-defined decisions. For tasks requiring detailed explanations, creative output, or complex multi-step reasoning, an LLM remains the more suitable tool.

What are confidence scores?

A key feature of Jev is that its outputs include probabilities and confidence information. These values help developers assess how strongly the model favours a particular answer.

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

However, confidence does not guarantee correctness. A model can be highly confident and still make an incorrect decision. Developers should test results against real examples and choose thresholds that suit the consequences of each decision.

It is also important to distinguish the probability of an answer from a separate confidence score. The exact meaning depends on the output type. Applications should follow the model's documentation rather than treating every returned number as the same measurement.

Where can decision models be useful?

Classification and routing

Many applications need to sort incoming information before processing it. A support platform might classify requests by topic and route them to the appropriate team. A business application could categorise documents, emails, or customer feedback.

Jev can return a category from a predefined list, allowing the application to route the item without parsing a generated paragraph.

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.

The results can be used to rank items, prioritise work, or identify cases requiring further review. The quality of these decisions still depends on the model and how well the criteria describe the task.

AI agents and tool selection

AI agents use models to decide which actions to take. An agent might choose a tool, classify a request, or determine whether a task needs additional information.

A general-purpose LLM can make these decisions as part of its reasoning. A decision model can handle a narrower choice before the agent acts.

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

Decision models should not be treated as security controls on their own. If an agent can execute commands or modify files, permissions and deterministic rules must enforce which actions are allowed. A model's classification can inform that process, but should not replace it.

Alternatives to Jev

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

OpenJev

OpenJev is an open project related to the Jev approach. It is worth examining if you want to explore an implementation outside a hosted service. Before using it, check its current availability, hardware requirements, supported features, and license.

Laya

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

These projects should be compared on actual performance, not just their stated capabilities. Use the same test data and measure accuracy, latency, resource requirements, and how well their confidence estimates match real outcomes.

Can you run decision models locally?

Running a model locally gives you more control over the software environment and where data is processed. However, the available deployment options differ by project. Jev is offered through an API, while open-source alternatives may provide options for running inference on your own hardware.

Hardware requirements depend on model size, memory use, and throughput. Some models can run on a CPU, while others benefit from a GPU, especially when processing many requests.

If you want to experiment with local AI models, DaDesktop provides cloud desktops with dedicated GPU options. You can use them for development and testing without buying a high-end GPU 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 useful when an application needs a defined answer that can trigger a specific next step. They can complement LLMs by handling classification, scoring, and routing while a generative model handles language and more complex reasoning.

The best choice depends on your application. Test the model against your own data, compare it with conventional classifiers and LLMs, and measure accuracy, latency, and cost. For important decisions, include human review and safeguards where needed.

Want to talk about your use case? Contact us and we'll point you in the right direction.