AI Decision Models: How They Differ from LLMs

Decision models have recently emerged as a distinct approach within the AI landscape. One prominent example, often discussed in this context, is Jev from TypeSafe AI. Unlike text-generating models, Jev generates structured choices, numerical scores, and confidence estimates.

Although traditional AI models have long handled predictions and classifications, Jev specifically focuses on producing structured decisions that software can utilize immediately. This methodology provides an alternative pathway for developing AI applications, particularly in scenarios where a system must evaluate data and determine the subsequent action.

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 process the response and validate its contents before implementation.

Jev operates differently. You supply context, define the specific questions requiring answers, and designate the expected answer type. In return, Jev delivers 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 an item against an ordered scale, for example, 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 pose multiple questions regarding the same input in a single request. For instance, a support application might classify a ticket, assess its urgency, and estimate the need for human review simultaneously. 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 is capable of explaining decisions, generating content, writing code, or reasoning through complex tasks. Jev, conversely, is engineered for focused decisions with clearly defined outputs.

This distinction is not that LLMs cannot make decisions. They are capable of classifying information and returning structured data. However, Jev is specifically built around this task, avoiding the generation of free-form responses that require application-level interpretation.

This makes Jev particularly useful for repeated, well-defined decisions. For tasks demanding 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 how strongly the model favours a particular answer.

For example, a customer support application might query 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 accuracy. 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 prior to 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 needing 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.

These results can be used to rank items, prioritize work, or identify cases requiring further review. The quality of these decisions still depends on the model and how effectively the criteria describe the task.

AI agents and tool selection

AI agents use models to decide which actions to take. An agent might choose a tool, classify a request, or determine whether a task needs additional information.

A general-purpose LLM can make these decisions as part of its reasoning process. A decision model can handle a narrower choice 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 assist in determining which workflow should handle a request. This would require a custom integration rather than relying on assumed native support.

Decision models should not be treated as security controls in isolation. 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 that process, but it 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 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 use, 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 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 match real outcomes.

Can you run decision models locally?

Running a model locally gives you more control over the software environment and where data is processed. However, available deployment options differ 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 use, and throughput. Some models can run on a CPU, while others benefit from a GPU, especially when processing many requests.

If you want to experiment with local AI models, DaDesktop provides cloud desktops with dedicated GPU options. You can use them for development and testing without buying 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 important decisions, include human review and safeguards where needed.

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