AI Decision Models: How They Differ from LLMs

Decision models have recently emerged as a distinct approach within the AI landscape. A prominent example generating significant discussion is Jev, developed by TypeSafe AI. Unlike traditional models, Jev generates structured choices, scores, and confidence estimates instead of plain text.

Although traditional AI models have long been used for predictions and classifications, Jev specializes in making structured decisions that software can integrate directly. This method provides an alternative pathway for building AI applications, particularly in scenarios where a system must assess information and determine the next logical action.

How does Jev work?

In a standard Large Language Model (LLM) setup, users submit a prompt and receive generated text. Even when JSON output is requested, applications typically need to parse and validate the response before utilizing it.

Jev operates with a different mechanism. Users supply context, define the specific questions to be answered, and specify the expected answer types. Jev then returns structured results that applications can process immediately without additional parsing.

Jev supports three primary question types:

• Choice: Selects an option from a predefined list, such as categorizing a request as billing, technical support, or sales.
• Score: Evaluates input against an ordered scale, such as assigning low, medium, or high priority.
• Yes or no: Estimates the probability that a statement is true, such as determining if a support ticket requires human intervention.

Multiple questions regarding the same input can be asked in a single request. For instance, a support application could classify a ticket, assess its urgency, and determine if human review is necessary simultaneously. Jev returns these results in a structured format, allowing the application to proceed with the appropriate next steps.

Decision models vs. LLMs

LLMs and decision models serve distinct purposes. LLMs excel at explaining decisions, generating content, writing code, and reasoning through complex tasks. Jev, conversely, is designed for focused decision-making with clearly defined outputs.

This distinction does not imply that LLMs cannot make decisions. They are capable of classifying information and returning structured data. However, Jev is specifically engineered for this task, eliminating the need for applications to interpret free-form responses.

This makes it highly effective for repetitive, well-defined decisions. For tasks requiring detailed explanations, creative outputs, or complex multi-step reasoning, LLMs remain 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 evaluating how strongly the model supports a particular answer.

For example, if a customer support application asks whether a ticket should be escalated and the model indicates a high probability of necessity, the application can route the ticket to a senior support team. Conversely, if the result is uncertain, the system can flag the ticket for manual human review.

It is important to note that confidence does not guarantee accuracy. A model may be highly confident yet still make an incorrect decision. Developers should validate results against real-world examples and set thresholds appropriate for the potential impact of each decision.

Additionally, it is crucial to differentiate between the probability of an answer and a separate confidence score. The precise meaning varies by output type. Applications should strictly 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 the sorting of incoming information prior to processing. Support platforms often classify requests by topic to route them to the appropriate teams, while business applications may categorize documents, emails, or customer feedback.

Jev can return a category from a predefined list, enabling applications to route items efficiently without needing to parse generated paragraphs.

Scoring and evaluation

Decision models can also evaluate information against defined scales. Applications can use them to rate support ticket urgency, score document relevance, or verify if responses meet specific criteria.

These results facilitate ranking items, prioritizing work, and identifying 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 which actions to execute. An agent might select a tool, classify a request, or ascertain if a task requires additional information.

General-purpose LLMs can make these decisions as part of their broader reasoning process. Decision models, however, can handle narrower choices prior to the agent taking action.

For instance, a custom workflow using Hermes Agent could leverage Jev to classify incoming tasks before handing them over to the agent. It could also assist in determining which workflow should handle a specific request. This typically requires custom integration rather than relying on assumed native support.

Decision models should not be relied upon as standalone security controls. If an agent has the capability to execute commands or modify files, permissions and deterministic rules must strictly enforce allowed actions. While a model’s classification can inform this process, it should not replace these rigid safeguards.

Alternatives to Jev

Jev belongs to a broader category of models designed to produce structured decisions. When comparing approaches, OpenJev and Laya are two other projects worth investigating.

OpenJev

OpenJev is an open project aligned with the Jev approach. It is suitable for those wishing to explore implementations outside of hosted services. Before deployment, verify its current availability, hardware requirements, supported features, and licensing 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 for those seeking to evaluate models that can run within their own environment.

These projects should be compared based on actual performance rather than just stated capabilities. Use consistent test data to measure accuracy, latency, resource consumption, and how well their confidence estimates align with real-world outcomes.

Can you run decision models locally?

Running models locally provides greater control over the software environment and data processing location. However, deployment options vary by project. While Jev is offered via an API, open-source alternatives may support inference on local hardware.

Hardware requirements depend on model size, memory usage, and throughput. Some models operate effectively on CPUs, while others benefit from GPUs, particularly when handling high volumes of requests.

For those wishing to experiment with local AI models, DaDesktop offers cloud desktops with dedicated GPU options. This allows for development and testing without the need to purchase high-end GPUs for local computers.

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 defined answers that trigger specific next steps. They complement LLMs by handling classification, scoring, and routing, while generative models manage language processing and complex reasoning.

The optimal choice depends on your specific application. Test models against your own data, compare them 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'll point you in the right direction.