AI Decision Models: Distinctive Differences from LLMs

Decision models have recently emerged as a distinct paradigm in AI. One prominent example is Jev by TypeSafe AI. Unlike traditional text generators, Jev outputs structured choices, scores, and confidence estimates directly.

While conventional AI models have long handled prediction and classification, Jev is designed to facilitate structured decisions that software can utilize immediately. This approach provides an alternative pathway for building AI applications, particularly when systems need to evaluate data and determine subsequent actions.

How does Jev work?

In a standard LLM workflow, you send a prompt and receive generated text. Even when requesting JSON output, the application typically needs to parse and validate the response before usage.

Jev operates differently. By providing context, defining specific questions, and specifying the expected answer type, you receive structured results that your application can process directly.

Jev supports three primary question types:

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

You can query multiple aspects of the same input in 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 proceed with the next step.

Decision models vs. LLMs

LLMs and decision models serve distinct purposes. An LLM is capable of explaining decisions, generating content, writing code, and reasoning through complex tasks. In contrast, Jev is tailored for focused decisions with predefined outputs.

This does not mean LLMs cannot make decisions; they can classify information and return structured data. However, Jev is specifically engineered for this task, eliminating the need for applications to interpret free-form responses.

This makes it particularly useful for repeated, well-defined decision processes. 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 information. These values assist developers in assessing the strength of the model's preference for a specific answer.

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

However, confidence does not guarantee accuracy. A model can be highly confident yet incorrect. Developers should validate results against real-world examples and select thresholds that align with the potential impact 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 all returned numerical values as identical measurements.

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 to direct them to the appropriate team. Similarly, business applications can categorize documents, emails, or customer feedback.

Jev can return a category from a predefined list, allowing applications to route items 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 document relevance, or evaluate whether a response meets specific criteria.

These results can be leveraged to rank items, prioritize work, or identify cases requiring further review. The quality of these decisions ultimately depends on the model's capabilities and how well the criteria describe the task.

AI agents and tool selection

AI agents utilize 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 broader reasoning, a decision model can handle narrower choices before the agent executes an 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 help determine which workflow should handle a request. This would require 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 produce 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 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 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 want to evaluate a model that can run in your own environment.

These projects should be compared based on actual performance, not just stated capabilities. Use the same 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 a model locally provides greater control over the software environment and data processing location. However, deployment options vary by project. Jev is available via 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 run on a CPU, while others benefit from a GPU, particularly when handling high volumes of requests.

If you are interested in experimenting with local AI models, DaDesktop offers cloud desktops with dedicated GPU options. These can be used for development and testing without purchasing high-end GPUs for local machines.

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

When should you use a decision model?

Decision models are advantageous 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, include human review and safeguards where necessary.

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