Large language models (LLMs) have made AI accessible to millions. But many business tasks do not require an AI to write paragraphs, explain its reasoning, or hold a conversation at all. Instead, they require a quick, reliable decision-maker. This is where decision models such as Jev and Laya come in, the former of which has truly made the industry look at an old problem in a new way.
How Large Language Models Work
An LLM generates text by predicting the next token which is a unit of text. It predicts a likely next token based on the input, adds it to the sequence, and predicts the next one. It keeps doing so until the response is complete.
For example, if you ask an LLM to classify a customer complaint as urgent or non-urgent, it might generate a response such as "This complaint should be considered urgent because..." before eventually returning its classification.
Generating text one token at a time is powerful because it allows LLMs to answer open-ended questions, write code, summarize documents, and tackle complex problems (basically the things we associate with AI assistants like ChatGPT).
However, it also introduces overhead when the task requires only a simple decision as LLMs may generate many tokens when a single label would suffice.
How Decision Models Differ from Large Language Models
Decision models evaluate inputs and return structured answers rather than generate text. They can classify information, select from predefined options, assign scores, or estimate probabilities.
Consider the same customer complaint as described above. A decision model can receive the complaint and a question asking whether it is urgent. It returns a decision and associated probabilities, without generating a written explanation.
An LLM produces a sequence of tokens, while a decision model can evaluate an input in a single model pass and return a structured result.
Why Jev Can Be Faster than Large Language Models
Jev, developed by TypeSafe AI, is a decision model built for these focused tasks. Instead of generating a response token by token, it returns typed decisions, such as yes/no answers, choices from a list, or scores.
This can substantially reduce latency and cost, particularly in applications that make many small decisions. For example, an AI agent might need to decide whether a document is relevant, which tool to use, or whether a request should be escalated. Using a general-purpose LLM for these decisions can add unnecessary processing time.
Decision models can also answer multiple predefined questions in one request. This reduces the overhead of making separate calls for related decisions.
Speed is not the only consideration, however. Decision models work best when the task and possible outputs are clearly defined. LLMs remain useful when a task requires open-ended reasoning, detailed explanations, or content generation.
From Jev to OpenAI and Microsoft
Major AI companies are taking notice of decision models, signaling a broader shift toward specialized AI systems.
- Jev: TypeSafe AI offers a hosted API for structured decisions, returning probabilities for predefined choices, scores, and yes/no questions.
- Laya: An open-weight alternative that developers can run themselves, offering a similar interface for decision-making tasks.
- OpenAI: Introduced a Decisions API this month, extending its offerings beyond conventional text-generating model interfaces.
- Microsoft: Announced Microsoft-Decision-1 this month, a model designed for classification, routing, prioritization, and workflow control.
These developments point to a future where AI applications use different models for different jobs rather than relying on one large model for everything.
Where Decision Models Are Useful
Common applications include fraud detection, customer support triage, lead scoring, document classification, content moderation, and AI agent orchestration. In each case, the system evaluates information and produces a decision that other software can act on.
The strongest applications are repetitive tasks with defined outputs, measurable success criteria, and a high volume of decisions.