Don't use your most powerful and expensive AI model for every task. A crucial skill is model triage: using cheaper models for simple, routine tasks like monitoring and scheduling, while saving premium models for complex reasoning, judgment, and creative work.

To control spiraling AI costs, teams should first determine if a task can be solved with deterministic, rules-based logic. Using AI for problems that have a straightforward, non-AI solution is an inefficient use of resources and introduces unnecessary variability and expense.

When a decision model makes a mistake, developers can debug it by analyzing its limited choices and logic, similar to fixing an if-else statement. This contrasts with generative LLMs, where fixing errors often involves guessing different prompts. This sense of control makes development more predictable and structured.

Don't default to the most powerful AI. A better architectural principle is to give each task the minimum model capability required to solve it reliably. This means knowing when a simple program is better than an SLM, or an SLM is better than a large model.

Unlike standard LLMs that generate text, Jev is optimized for making choices from predefined options (e.g., yes/no, 1-10 scale, pick from a list). This makes it a "System 1" model, ideal for high-speed classification, routing, and filtering tasks that serve as smart "if" statements within larger applications.

Resist the urge to apply LLMs to every problem. A better approach is using a 'first principles' decision tree. Evaluate if the task can be solved more simply with data visualization or traditional machine learning before defaulting to a complex, probabilistic, and often overkill GenAI solution.

Jev can be layered on top of other tools or models to create a navigation or routing system. It can parse user input to determine which tool to activate and what action to perform, effectively directing traffic within a complex application or agentic system at near-zero latency.

It's tempting to think you can intuit the few factors a decision hinges on. This is often wrong. Complex systems have non-obvious leverage points. The process of building an explicit model reveals which variables have the most impact—a discovery you can't reliably make with intuition alone.

The optimal way to use decision models like Jev is to break large problems into many small, independent questions. This contrasts with stuffing everything into a single LLM prompt. This decomposition makes each AI-driven step verifiable, measurable, and debuggable, leading to more reliable and maintainable software.