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 failure mode isn’t latency or cost — it’s hallucinations that sound confident and pass surface checks. Prompt engineering helps, but it doesn’t scale once systems grow long-running, tool-using, or multi-agent.

Verdic takes a different approach:

Define an explicit intent + scope contract for what the model is allowed to output

Validate LLM outputs before execution, not just inputs

Block or flag responses that drift semantically, contextually, or domain-wise

Keep enforcement deterministic and auditable (not “best effort” prompts)

It’s designed to sit between the LLM and your application, acting as a guardrail rather than another model.

This is still early, and I’m especially interested in feedback on:

Where this breaks down in real systems

How teams currently handle hallucinations beyond prompts

Whether deterministic enforcement is useful or too restrictive in practice

Site: https://www.verdic.dev

Happy to answer questions or share implementation details if useful.