AI Decision Models: Understanding Their Distinct Role Compared to LLMs

AI decision models have recently emerged as a distinct approach to artificial intelligence. One notable example receiving significant discussion is Jev from TypeSafe AI. Unlike traditional text-generating models, Jev delivers structured choices, scores, and confidence estimates rather than free-form text.

Although conventional AI models have long excelled at prediction and classification, Jev is specifically engineered to produce structured decisions that software systems can utilize directly. This methodology provides an alternative pathway for building AI applications, particularly in scenarios where a system must assess information and determine the subsequent action.

How does Jev operate?

In a standard LLM workflow, you submit a prompt and receive generated text in return. Even if you request JSON output, the application must still parse the response and validate its structure and content before it can be safely used.

Jev functions differently. By providing context, defining specific questions, and specifying the expected answer type, you allow Jev to return structured results that your application can process immediately without additional parsing.

Jev supports three primary question types:

• Choice: Selects a specific option from a predefined list, such as billing, technical support, or sales.
• Score: Evaluates an item against an ordered scale, such as low, medium, or high priority.
• Yes or no: Estimates the probability of a statement being true, for example, whether a support ticket requires human intervention.

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

Decision models vs. LLMs

LLMs and decision models serve distinct purposes. An LLM is capable of explaining decisions, generating content, writing code, and engaging in complex reasoning. Jev, on the other hand, is designed for focused decision-making with strictly defined outputs.

The distinction does not imply that LLMs cannot make decisions; they can classify information and return structured data. However, Jev is purpose-built for this task, eliminating the need to interpret free-form responses within the application logic.

This specialization makes it highly effective for repeated, 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 assessing the strength of the model’s preference for a particular answer.

For example, a customer support application might query whether a ticket requires escalation. If the model indicates a high probability that escalation is necessary, the application can route the ticket to a senior support team. If the result is uncertain, it can trigger a human review process.

However, high confidence does not guarantee correctness. A model can be highly confident in an incorrect decision. Developers should test results against real-world examples and set thresholds that account for the potential consequences of each decision.

It is also crucial to distinguish between the probability of an answer and a separate confidence score. The specific meaning depends on the output type. Applications should adhere to the model's documentation rather than treating every returned numeric value as an identical metric.

Where can decision models be useful?

Classification and routing

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

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

Scoring and evaluation

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

These results can be leveraged to rank items, prioritize tasks, or identify cases requiring further review. The quality of these decisions relies 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 tool, classify a request, or decide if a task requires additional information.

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

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 request. Note that this requires 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 it 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 aligned with the Jev approach. It is worth examining if you wish to explore an implementation outside of a hosted service. Before adoption, verify its current availability, hardware requirements, supported features, and license terms.

Laya

Laya is an open-source decision model from 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 want to evaluate a model that can run in your own environment.

These projects should be compared based on actual performance, not just their 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 available through an API, while 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 processing high volumes of 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 ideal when an application requires a defined answer that can trigger a specific next step. They complement LLMs by handling classification, scoring, and routing, while a generative model manages language and more complex reasoning.

The optimal choice depends on your application's needs. 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.