kundan_s__r

Hi HN, For those running LLMs in real production environments (especially agentic or tool-using systems): what’s actually worked for you to prevent confident but incorrect outputs? Prompt engineering and basic filters…

I’ve been working on Verdic Guard, a validation layer for production LLM systems where prompts, filters, and monitoring aren’t enough. In many real deployments (fintech, enterprise workflows, agentic systems), the…

I’m building Verdic Guard to explore a problem I kept seeing with LLMs in production. Models often behave well in demos and short interactions, but once they’re embedded into long, agentic, or real-world workflows,…

I’m building Verdic Guard, an experiment around a problem I kept running into while working with LLMs in production. LLMs usually behave well in demos and short interactions, but once they’re embedded into long,…

We’ve been working on production LLM systems and noticed a recurring issue: even well-crafted prompts fail under real-world conditions. We wrote a technical breakdown of the failure modes (intent drift, hallucinations,…

We built Verdic (https://www.verdic.dev/ )after repeatedly running into the same issue while deploying LLMs in production: most AI failures aren’t about content safety, they’re about intent drift. As models become more…