Compared Jev and Claude Opus 5 on 3,080 support tickets. Sending only Jev's low-confidence answers to Opus yields 84.0% accuracy while still being 3.5x cheaper than Opus alone. Discusses three deployment strategies: Opus for everything, Jev for everything, or Jev first and Opus when unsure.
Jev vs Claude Opus 5 on 3,080 support tickets. Send only Jev's low-confidence answers to Opus → 84.0% accuracy, still 3.5x cheaper than Opus alone. Which would you ship? 1. Opus for everything 2. Jev for everything 3. Jev first, Opus when unsure #LLM #AIEngineering
The author shares a Jev usage insight: it seemed tricky at first, but clicked when framed as 'not letting the AI write text.' Using inquiry handling as an example, the conventional approach dumps everything on GPT or Claude to analyze, while Jev takes a different approach.
The prototype uses AI to estimate hesitation or interest from cursor back-and-forth movements or lingering before clicking, observing customer demeanor and supplementing explanations or suggesting comparisons.
The author built an e-commerce real-time customer service demo using Jev and gpt-live-1, which instantly suggests recommended products based on the user's speech during a conversation, delivering a great experience.
