What Jev is: structured decisions instead of long-form output
Jev takes an input, a defined question and returns structured predictions with confidence scores, and Oracle's guide shows it governing which agent memories to keep.
𝗝𝗲𝘃 𝗖𝗹𝗲𝗮𝗿𝗹𝘆 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱. What is it? How does it work? Why is everyone talking about it? Jev is designed to evaluate inputs and return structured decisions without generating long-form responses. Here's the basic flow: 1) Provide an input ↳ Send the text or data you want evaluated. 2) Define your questions ↳ Specify the decisions or classifications you need. 3) Get structured predictions ↳ Jev returns answers with confidence scores. This makes it useful for classification, relevance scoring, and routing. The advantage? Faster, cheaper decisions without the overhead of generating long-form responses. The 𝗼𝘂𝘁𝗽𝘂𝘁 𝗶𝘀 𝗮 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 your application can act on, 𝗿𝗮𝘁𝗵𝗲𝗿 𝘁𝗵𝗮𝗻 𝗮 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝗱 𝗲𝘅𝗽𝗹𝗮𝗻𝗮𝘁𝗶𝗼𝗻. One use case for these structured decisions is managing AI agent memory. Instead of storing every interaction, an agent needs to evaluate which information is relevant, trustworthy, and worth remembering. Jev can help make those decisions, while a database stores the memories and their supporting evidence. Oracle's technical guide walks through exactly that. 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗴𝘂𝗶𝗱𝗲 →https://lucode.co/guide-jev-oracle-db-govern-agent-memory-z7xd What else would you add? —— ♻️ Repost to help others learn Jev. 🙏 Thanks to @Oracle Developers for sponsoring this post. ➕ Follow me ( Nikki Siapno ) to improve at AI and system design.