AI Decision Models: How They Differ from LLMs

Recently, decision models have emerged as a distinct approach within the AI landscape. A notable example receiving considerable attention is Jev, developed by TypeSafe AI. Unlike traditional models, Jev generates structured choices, scores, and confidence estimates rather than free-flowing text.

While conventional AI models have long been used for predictions and classifications, Jev is specifically designed to make structured decisions that software can execute directly. This offers an alternative pathway for building AI applications, particularly when systems require the ability to evaluate information and determine subsequent actions.

How does Jev work?

In a standard LLM interaction, you submit a prompt and receive generated text. Even if you request JSON output, your application must still parse and validate the response before it can be used.

Jev operates differently. You provide context, define the specific questions to be answered, 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 an item against an ordered scale, such as low, medium, or high priority.
• Yes or no: Estimates the probability of a statement's truth, such as determining if a support ticket requires human attention.

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 the likelihood that human review is needed, all at once. Jev returns these 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. In contrast, Jev is engineered for focused decisions with clearly defined outputs.

This distinction does not imply that LLMs are incapable of making decisions; they can also classify information and return structured data. However, Jev is built specifically around this task, eliminating the need for applications 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 metrics. These values assist developers in assessing the strength of the model's preference for 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 necessary, the application could route the ticket to a senior support team. Conversely, if the result is uncertain, it might direct the ticket for human review.

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

It is also crucial 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 numbers 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 classify requests by topic and route 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 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, prioritize tasks, or identify cases requiring further review. The quality of these decisions depends on the model and how accurately the criteria describe the task.

AI agents and tool selection

AI agents use models to determine which actions to take. 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 narrower choices before the agent acts.

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 help determine which workflow should handle a request. Note that this would require a custom integration rather than relying on assumed native support.

Decision models should not be treated as standalone security controls. 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 this process but should not replace it.

Alternatives to Jev

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

OpenJev

OpenJev is an open project related to the Jev approach. It is worth examining if you wish to explore an implementation outside of 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 based 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 align with real outcomes.

Can you run decision models locally?

Running a model locally offers greater control over the software environment and data processing location. However, deployment options vary 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 usage, and throughput. Some models can run on a CPU, while others benefit from a GPU, especially when handling many requests.

If you wish to experiment with local AI models, DaDesktop provides cloud desktops with dedicated GPU options. You can use these for development and testing without purchasing 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 critical decisions, include human review and safeguards where necessary.

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