AI Decision Models: How They Differ from LLMs

Recently, decision models have emerged as a distinct paradigm in the field of artificial intelligence. One notable example that has attracted significant discussion is Jev by TypeSafe AI. Unlike traditional models, Jev generates structured choices, scores, and confidence estimates rather than free-form text.

Although conventional AI models have long been used for predictions and classifications, Jev is specifically engineered to produce structured decisions that software systems can utilize directly. This methodology presents an alternative pathway for developing AI applications, particularly in scenarios where a system must evaluate data and determine the subsequent action to take.

How does Jev work?

In a standard Large Language Model (LLM) workflow, users submit a prompt and receive generated text. Even when JSON output is requested, the application layer must still process and validate the response before it can be utilized effectively.

Jev operates on a different principle. Users provide context, define specific questions to be answered, and specify the expected data type. In return, Jev delivers structured results that can be integrated directly into the application logic without further parsing.

Jev accommodates three primary question formats:

• Choice: This mode selects an option from a user-defined list, such as billing, technical support, or sales.
• Score: This mode evaluates input against an ordered scale, such as low, medium, or high priority.
• Yes or No: This mode estimates the probability that a statement is true, for instance, determining if a support ticket requires human intervention.

Multiple queries regarding the same input can be executed within a single request. For instance, a support application could simultaneously classify a ticket, rate its urgency, and assess the need for human review. Jev returns these results in a structured format, enabling the application to determine the next course of action efficiently.

Decision models vs. LLMs

While LLMs and decision models serve different functions, they are not mutually exclusive. LLMs excel at explaining decisions, generating content, writing code, and performing complex reasoning. In contrast, Jev is optimized for focused decision-making with clearly defined outputs.

The key distinction lies not in the capability of LLMs to make decisions—they can certainly classify information and return structured data—but in their design focus. Jev is purpose-built for these tasks without generating free-form responses that require additional interpretation by the application.

This makes decision models particularly valuable for repetitive, well-defined tasks. Conversely, for requirements involving detailed explanations, creative output, or complex multi-step reasoning, an LLM remains the more appropriate tool.

What are confidence scores?

A defining feature of Jev is the inclusion of probabilities and confidence metrics in its outputs. These values assist developers in gauging the strength of the model’s preference for a particular 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 system could route the ticket to a senior support team. If the outcome is uncertain, the application might flag it for human review.

It is crucial, however, to recognize that high confidence does not guarantee accuracy. A model may be highly confident yet still incorrect. Developers should validate results against real-world examples and establish thresholds that align with the potential consequences of each decision.

Furthermore, it is important to differentiate between the probability of an answer and a distinct confidence score. The precise interpretation varies by output type, and 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 the sorting of incoming data before further processing. For instance, a support platform may categorize requests by topic to direct them to the appropriate team, while business applications might classify documents, emails, or customer feedback.

By returning a category from a predefined list, Jev allows applications to route items seamlessly without the need to parse generated paragraphs.

Scoring and evaluation

Decision models are also effective for evaluating information against a defined scale. An application might rate the urgency of a support ticket, score a document for relevance, or verify if a response meets specific criteria.

These results can facilitate ranking items, prioritizing work, or identifying cases requiring deeper inspection. The quality of these decisions, however, depends heavily on the model’s capabilities and how accurately the criteria define the task.

AI agents and tool selection

AI agents utilize models to determine which actions to execute. An agent might select a specific tool, classify a request, or ascertain if a task requires additional information.

While a general-purpose LLM can perform these decisions as part of its broader reasoning process, a dedicated decision model can handle narrower, specific choices prior to the agent acting.

For instance, a custom workflow utilizing Hermes Agent could employ Jev to classify an incoming task before passing it to the agent. It could also assist in determining which workflow should process a request, though this would necessitate a custom integration rather than relying on presumed native support.

It is important to note that decision models should not be relied upon as standalone security controls. If an agent has the ability 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 robust security mechanisms.

Alternatives to Jev

Jev belongs to a broader category of models designed to generate structured decisions. Other notable projects in this space include OpenJev and Laya, which offer different approaches worth exploring.

OpenJev

OpenJev is an open project aligned with the Jev methodology. It presents an interesting option for those wishing to explore implementations outside of hosted services. Before adopting it, it is advisable to 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. This model may be relevant for teams seeking to evaluate models that can be deployed in their own environments.

When comparing these projects, focus on actual performance metrics rather than just stated capabilities. Use consistent test data to measure accuracy, latency, resource consumption, and the correlation between confidence estimates and real-world outcomes.

Can you run decision models locally?

Deploying models locally offers greater control over the software environment and data processing location. However, deployment options vary by project. While Jev is provided via API, open-source alternatives may support local inference on your own hardware.

Hardware requirements are influenced by model size, memory usage, and throughput. Some models operate effectively on CPUs, while others benefit significantly from GPU acceleration, particularly under high request loads.

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

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

When should you use a decision model?

Decision models are particularly useful when an application requires a defined answer that triggers a specific subsequent step. They serve as a complement to LLMs, handling classification, scoring, and routing tasks while generative models manage language generation and complex reasoning.

The optimal choice depends on the specific application context. It is recommended to test the model against your own data, compare it with conventional classifiers and LLMs, and measure accuracy, latency, and cost. For critical decisions, incorporating human review and safety safeguards is essential.

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