AI Decision Models: How They Differ from LLMs

Decision models have emerged as a distinct paradigm in AI development. A prominent example is Jev by TypeSafe AI, which generates structured choices, scores, and confidence levels instead of traditional text output.

Although conventional AI models have long handled predictions and classification, Jev specializes in delivering structured decisions that software systems can utilize immediately. This methodology offers an alternative path for building AI applications, particularly when a system must assess data and determine the appropriate next step.

How does Jev function?

In a standard LLM workflow, you submit a prompt and receive generated text. Even if you request JSON, the application typically needs to process and validate the response before it can be used.

Jev operates differently. By providing context, defining specific questions, and specifying the expected answer type, you receive structured results that your application can process without intermediate steps.

Jev supports three primary question types:

• Choice: Selecting an option from a predefined list, such as billing, technical support, or sales.
• Score: Rating an item against an ordered scale, like low, medium, or high priority.
• Yes or no: Estimating the probability of a statement's truth, such as whether a support ticket requires human intervention.

You can pose multiple questions regarding the same input within a single request. For instance, a support application could classify a ticket, assess its urgency, and determine if it needs human review in one go. Jev returns these results in a structured format, enabling the application to proceed with the next action.

Decision models vs. LLMs

LLMs and decision models serve distinct roles. While LLMs excel at explaining decisions, generating content, writing code, and complex reasoning, Jev is engineered for focused decisions with predefined 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 architected for this task, eliminating the need to interpret free-form responses.

This makes it particularly effective for repetitive, well-defined decisions. For tasks requiring detailed explanations, creative output, or complex multi-step reasoning, LLMs remain the superior choice.

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 of need, the application can route the ticket to a senior team. If the result is ambiguous, it can direct the ticket for human review.

It is crucial to note that confidence does not guarantee accuracy. A model can be highly confident yet incorrect. Developers should validate results against real-world examples and set thresholds that align with the consequences of specific decisions.

Additionally, it is important to differentiate between the probability of an answer and a distinct confidence score. The precise meaning varies by output type. Applications should adhere to the model's documentation rather than assuming all returned numerical values represent the same 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 and route them to the appropriate team. Similarly, business applications can classify documents, emails, or customer feedback.

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

Scoring and evaluation

Decision models can also assess information against a defined scale. An application could rate ticket urgency, score document relevance, or evaluate if a response meets specific criteria.

These results can facilitate ranking, prioritizing work, or identifying cases needing further review. The quality of these decisions relies on the model and how accurately the criteria define the task.

AI agents and tool selection

AI agents use models to determine actions. An agent might select a tool, classify a request, or decide if a task requires more information.

While general-purpose LLMs can make these decisions during reasoning, a decision model can handle narrower choices before the agent acts.

For instance, a custom workflow using Hermes Agent could leverage Jev to classify an incoming task before passing it to the agent. It could also help determine which workflow should handle a request. This 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 allowed actions. 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 other notable projects worth exploring when comparing approaches.

OpenJev

OpenJev is an open project related to the Jev approach. It is worth examining if you wish to explore an implementation outside a hosted service. Before deployment, 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 like 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 rather than stated capabilities. Use consistent test data to measure accuracy, latency, resource requirements, and the alignment of confidence estimates 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 available via 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, especially when handling high request volumes.

If you wish to experiment with local AI models, DaDesktop provides cloud desktops with dedicated GPU options. This allows for development and testing without the need to purchase high-end local hardware.

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 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 appropriate.

Want to discuss your use case? Contact us and we will guide you in the right direction.