Are you using a full large language model to make decisions that only require a simple yes, no, or classification?
When an LLM has to interpret multiple conditions, decide what to do, and then generate an explanation, it can consume more tokens and computation than the decision itself requires.
A 2026 study estimated frontier-scale LLM inference at about 0.31 Wh per standard query and found that reasoning queries producing around 5,000 output tokens increased energy use by approximately 13×. The study also found that inference energy rises roughly with the amount of generated output, highlighting the computational cost of making an LLM generate more than a task actually requires (Source).
This is where Jev AI fits into Knolli. Jev is designed specifically for decision-making, allowing certain decisions to be separated from the LLM’s generation process. By integrating Jev, Knolli can reduce complex conditional prompts, use more focused system prompts, and avoid asking a general-purpose LLM to handle decision logic when a direct decision is all that is needed.
Jev AI is a decision-making model designed for tasks where an application needs a clear answer rather than a long generated response. General-purpose LLMs can handle these decisions, but they may take several seconds and generate additional text that the application does not actually need.
Jev focuses on the decision itself. This makes it useful for workflows where AI needs to classify an input, choose between possible outcomes, or determine what should happen next before another model generates a response.
When a task only requires a direct decision, generating several sentences of explanation adds unnecessary processing and token usage.
Jev can handle the decision without producing that additional output. This allows applications to use a decision-focused model for the classification step instead of relying on a general-purpose LLM for both decision-making and text generation.
Jev can be used for straightforward decisions such as:
These tasks primarily require classification rather than content generation. Separating the decision from the generated response allows Knolli to use the appropriate model for each part of the workflow.
A single LLM prompt can become difficult to manage when it contains many conditional instructions. For example, one system prompt may tell the model to follow different instructions depending on the user type, intent, or situation.
Jev allows that conditional logic to be separated from the main generation prompt. Instead of placing every possible branch inside one prompt, the decision can be handled first, and the relevant prompt can then be used for the required outcome.
Consider a system prompt containing instructions such as:
This logic can be divided into separate prompts, with each prompt written for one specific outcome. Jev determines which condition applies, so the LLM does not need to interpret the entire decision tree inside a single prompt.
Splitting conditional logic also reduces the amount of instruction text that needs to be included in every request.
Rather than repeatedly sending instructions for all possible scenarios, the LLM receives only the prompt relevant to the selected path. This can reduce input-token usage, particularly when the original system prompt contains many conditional branches.
Once each conditional path has its own prompt, system instructions can be written specifically for that output.
A prompt no longer needs to account for several different behaviors at once. It can define only the instructions, context, and response requirements relevant to one task, making the prompt more precise and easier to control.
Knolli already connects copilots with business data, applications, infrastructure, workflow tools, and multiple AI models. Its current integration ecosystem includes:
Jev adds another decision-making option within this ecosystem. Knolli can use Jev alongside its own custom classification models, depending on the knowledge required for a particular decision.
Some decisions depend on broader knowledge that is not available in a company’s internal systems.
For example, if a heat-sensitive traveler wants to know whether March or June is a better time to visit India, the decision depends on general knowledge about climate and seasonal conditions.
Jev is suited to this type of decision because the answer requires context beyond private business data.
Other decisions depend entirely on information specific to a business. Lead qualification is a clear example. Whether a prospect is a good fit may depend on the company’s own criteria, customer profile, or internal business rules.
For these use cases, Knolli can build custom classification models that remain private and run on the customer’s hardware.
Custom classification models can also be a strong fit when the classification problem is relatively narrow. As a practical guideline, when there are fewer than roughly 50 intents and the required knowledge is private, a custom model may be more suitable.
This is not a fixed technical limit. It is an approximate guideline for deciding when a focused private classifier can handle the task effectively.
Integrating Jev AI gives Knolli another way to handle decision-driven tasks without relying on the same model for every type of decision. Jev can support decisions that require broader knowledge, while Knolli’s custom classification models remain suited to private, narrowly defined classification problems.
If you want to use Jev AI for your specific use case, reach out to the Knolli team. We can work with you to identify where Jev fits into your workflow and help optimize cost, token usage, and the decision-making setup around your requirements.
Jev AI can handle decision-making tasks before the LLM generates a response. This allows Knolli to use more focused prompts and separate classification from generation when a workflow only needs a direct decision first.
General-purpose LLMs may take several seconds to make a decision and can generate more text than the task requires. Jev is designed specifically for decision-making, making it more suitable for tasks that need a direct classification or outcome.
Jev can move conditional decision logic outside a large LLM prompt. Instead of sending multiple if/else instructions every time, the system can use smaller, more focused prompts for the selected outcome, reducing unnecessary input tokens.
Knolli’s custom classification models are designed for decisions based on private, customer-specific knowledge. They can remain private, run on the customer’s hardware, and are especially suitable for focused classification tasks with a relatively limited number of intents.
If you want to implement Jev to optimize your operations, you can reach out directly to the Knolli team. They will work with you to architect your workflows, optimize your token usage, and significantly drive down your overall AI infrastructure costs.
