Today’s large language models are verbose, even if they’re being asked to recommend a simple decision, driving up usage costs through the sheer volume of tokens they consume or generate. Enterprises looking to incorporate AI into automated workflows will want something less verbose — both because machines are often just looking for a categorical answer, and because automated workflows are likely to result in far greater volumes of decisions, and thus token consumption, than human-mediated workflows.
TypeSafe AI, a startup founded by former OpenAI researcher and RLHF co-inventor Diogo Almeida, thinks it can help with a new LLM, Jev, which generates responses that can be consumed directly by software applications or other AI models as part of an automated workflow: which tool to invoke, which action to take next, whether a request should be approved, or when a task should be handed off to another model.
Jev takes the current state of a task or workflow as input and returns a defined decision, along with the probability of that decision, in a concise response rather than generating a long sequence of tokens to express an answer in natural language, Almeida wrote in a blog post.
