AI Decision Models: How They Differ from LLMs

Decision models have recently emerged as a distinct paradigm in AI. A prominent example is Jev from TypeSafe AI, which generates structured choices, scores, and confidence estimates instead of natural language text.

Although traditional AI models have long been used for predictions and classifications, Jev is specifically engineered to make structured decisions that software can execute directly. This approach provides an alternative pathway for building AI applications, particularly in scenarios where a system must evaluate data and determine subsequent actions.

How does Jev work?

In a standard Large Language Model (LLM) workflow, you submit a prompt and receive generated text. Even when requesting JSON, the application must still parse, validate, and process the response before it can be utilized.

Jev operates differently. You supply context, define the specific questions to be answered, and specify the expected output types. In return, Jev delivers structured results that your application can process immediately without additional interpretation.

Jev supports three primary question types:

• Choice: Selects an option from a predefined 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 that a statement is true, such as whether a support ticket requires human intervention.

Multiple questions can be asked about the same input in a single request. For instance, a support application could simultaneously classify a ticket, rate its urgency, and estimate whether it needs human review. Jev returns these results in a structured format, allowing the application to determine the next steps.

Decision models vs. LLMs

LLMs and decision models serve distinct functions. While an LLM can explain decisions, generate content, write code, or perform complex reasoning, Jev is optimized for focused decisions with defined outputs.

The difference is not that LLMs are incapable of making decisions; they can certainly classify information and return structured data. However, Jev is architected specifically for this task, eliminating the need for applications to interpret free-form responses.

This makes Jev particularly valuable for repetitive, well-defined decision tasks. 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 metrics help developers gauge how strongly the model favors 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 can route the ticket to a senior support team. Conversely, if the result is uncertain, the system can flag it for human review.

However, confidence does not guarantee accuracy. A model can be highly confident yet still make an incorrect decision. Developers should validate results against real-world examples and set 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 specific meaning depends on the 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 it. A support platform might categorize requests by topic and route them to the appropriate team, while a business application could classify documents, emails, or customer feedback.

Jev can return a category from a predefined list, enabling the application to route the item without needing to parse generated paragraphs.

Scoring and evaluation

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

These results can be leveraged to rank items, prioritize workflows, or identify cases requiring further scrutiny. The quality of these decisions relies on the model's capability and how accurately the criteria describe the task.

AI agents and tool selection

AI agents utilize models to determine actions. An agent might select a tool, classify a request, or decide 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 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 specific request. Note that this would require a custom integration rather than relying on assumed native support.

Decision models should not be viewed as security controls in isolation. 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 it should not replace it.

Alternatives to Jev

Jev belongs to 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 aligned with the Jev approach. It is worth examining if you wish to explore an implementation outside of a hosted service. Before deployment, verify its current availability, hardware requirements, supported features, and licensing 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 intend to evaluate a model that can run in your own environment.

These projects should be compared based on actual performance rather than just stated capabilities. Use identical test data to measure accuracy, latency, resource requirements, and how well their confidence estimates align with real-world 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 provided via an API, whereas 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 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 purchasing a high-end GPU for your local machine.

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

When should you use a decision model?

Decision models are beneficial when an application requires a defined answer that can trigger a specific next step. They complement LLMs by handling classification, scoring, and routing, while generative models manage language generation 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.