Jev is not suitable for chatting, writing articles, or directly generating code. It lacks free text generation capability and excels at quick judgments within predefined options, levels, or unconditional situations. Forcibly breaking characters into large numbers of Choices may theoretically spell text, but the effect is poor, slow speed, and contradicts product design.
The correct usage is not to let JEV replace the generative model, but to place it before and after the generation process: decide which model or tool to call before generation, and after generation, determine whether the result is compliant and whether manual review is needed.
Why outputting structured results is still useful
Many software steps don't need a nice answer, but rather a choice that the program can execute. The customer service system wants to know which queue the ticket goes to, the risk control system wants to know the risk level, and the agent wants to know whether to call a particular skill. The candidate answers for these tasks are already limited, and generating a text and then parsing it actually increases the points of failure.
JEV is more like an intelligent judgment function with probabilistic capabilities rather than an assistant that can express freely.
These types of needs should be replaced with different tools
- Writing articles, emails, and summaries: Using generative large models, Jev can handle content classification or risk screening.
- Generate code: Using code models or programming agents, JEV can determine task types, select tools, or review strategies.
- Continuous Dialogue: Contextually maintained by the chat model, JEV is suitable for routing intent behind each round.
- Images, audio, and video: first convert multimodal models into text or structured fields; JEV 1.13 only receives text.
- Precise computation: Completed directly with code, the model only judges semantic conditions that are difficult to hardcode.
Combining them is more reasonable than choosing one or the other
For example, when a customer service robot receives a message, the JEV first determines the department, urgency, and whether a human intervention is needed; For ordinary issues, the chat model drafts the response, while for high-risk issues, only internal summaries are generated and forwarded to humans. In this way, the JEV is not responsible for writing the response, and the generative model does not decide on high-risk actions alone.
The same applies in Programming Agents: Jev can select the most relevant item from the skill list, the code loads the tool, and the code model then performs modifications. Each module only does what it does best, and the system is usually easier to test and maintain than "letting one model handle the entire process."
Decide whether to use JEV's quick method
Ask yourself first: Can the result be listed as a limited option in advance and the code can act accordingly? If the answer is yes, you can design a Choice, Score, or Noul; If the answer is no, especially when original text, open reasoning, or unknown entities are needed, the generative model should handle it. Don't force JEV tasks that don't belong to the decision model just because it's fast and cheap.
