Daniel Matos

12M+ views so I had to dig into this Is it just a hyped launch or something novel and actually useful? I’m leaning to the latter and will break it down with a practical example First, understand that Jev isn’t a typical LLM that you prompt and get back a written response It’s much more specific - you give Jev context along with a set of questions and possible answers and it returns probabilities to each question and answer set, and it does it super fast and cheap I found it helpful to think through a simple example: Let’s say you’re an online retailer and you get hundreds of inquiries a day You put an agent in the workflow that looks at each inquiry and makes a judgement call on the next action and then generates a response But that LLM judgement call can be messy - LLMs still have a tendency to make shit up or respond in ways that you don’t like, so we put humans in the loop What Jev does is look at each inquiry and makes a fast decision on the nature of it so the workflow can take the next best action with confidence It doesn’t replace the workflow or the LLM entirely, it handles the fuzzy fork in the road that used to require a human to read the message first Let’s say you get a customer email: “This jacket sucks! The zipper jammed the first time I wore it and now it won’t close so I want my money back.” That message lands in the retailer’s system along with a few facts the company already has: the order is 11 days old, the return window is 30 days, the jacket was $100, and this customer has one previous order with no refunds In a typical LLM workflow you’d let the model decide what to do and have it initiate that action and send a reply email, BUT many companies want a HITL in many of these cases, especially ones where it’s not entirely clear what to do Imagine the LLM replies to the email with troubleshooting instructions on how to fix the zipper - probably not the best action With Jev, the company doesn’t ask it to write a reply, it asks a few specific questions about the email and order >Does the customer want a refund? >What is the main issue: defective product, didn’t like it, late delivery, or something else? >How unhappy does the person sound? >Does this look like it falls inside the published return policy? >Should this go to returns, quality control, or a human agent? Jev looks at the email and the order context, then answers all of those at the same time with a probability score - no prose, just a score In this case the picture comes back fairly clear: yes, this is a refund request, the issue is a defective product, frustration is high but not explosive, the request is inside the return window, returns should own it That is enough for the retailer’s existing rules to take the next action with confidence If Jev had been less sure - say the email was sarcastic, the order was 45 days old, or it was unclear whether the customer wanted a refund or a replacement - the workflow could put the ticket in a human queue instead of guessing The useful part is the handoff, specifically knowing when to pass it to the next step in a workflow or route to HITL If Claude were the judge in a less clear situation, you’re likely to get a long winded answer that still leaves you wondering what to do next, and it would have taken longer and cost a lot more This was a simple example but I can see how this can be incredibly useful in complex enterprise workflows which is why I lean towards novel and useful over just a hyped new thing Will be following this one closely