Post by Udit Jain

We have CI/CD for code. We have monitoring for APIs. But for AI applications, system prompts are still often validated manually, based on intuition.  What if prompts could be tested the same way we test code? I wanted to bring software engineering discipline to prompt development. So as my final-year college project, I built 𝗣𝗿𝗼𝗺𝗽𝘁𝗢𝗽𝘀 - a prompt evaluation and observability platform for AI applications. PromptOps provides a structured and measurable way to evaluate and observe system prompts across the entire LLM lifecycle, turning prompt development from intuition-driven experimentation into an evidence-driven engineering workflow. 🧪 𝗗𝘂𝗿𝗶𝗻𝗴 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 - 𝗥𝗲𝗽𝗹𝗮𝗰𝗲 𝗚𝘂𝗲𝘀𝘀𝘄𝗼𝗿𝗸 𝘄𝗶𝘁𝗵 𝗘𝘃𝗶𝗱𝗲𝗻𝗰𝗲  • Create reusable evaluation datasets (single-turn and multi-turn conversations)  • Compare multiple prompt candidates side-by-side on same inputs under identical conditions  • Validate outputs using built-in rules (text match, regex, length checks) or custom sandboxed JavaScript evaluators  • Analyze detailed reports with pass rates, evaluator breakdowns, and failed-case inspection 📡 𝗔𝗳𝘁𝗲𝗿 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 - 𝗢𝗯𝘀𝗲𝗿𝘃𝗲 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲  • Route production traffic through PromptOps with a single configuration change (zero SDK lock-in)  • Capture requests, responses, latency, token usage, status, and environment metadata in the background  • Automatically run attached evaluators on live outputs to detect regressions in real-time 🔄𝗖𝗹𝗼𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗟𝗼𝗼𝗽 - 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁  • Turn production failures directly into new test cases in your evaluation datasets  • Use an AI-assisted prompt improvement workflow to suggest optimized system prompts based on concrete evaluation evidence  • Review prompt diffs manually before adoption - nothing is auto-deployed Every failure becomes new test data, creating a continuous improvement loop driven by measurable results rather than assumptions. If you're interested, you can check out the detailed technical writeup here: 👉 https://lnkd.in/gaxMbuWi