Hey all! Need some real-world advice. My team's building a customer-facing AI agent for a regulated industry (finance). The compliance team is asking for proof that our agent's outputs are consistent, safe, and traceable before launch.
I know LangSmith is great for debugging, but how do we use it to *prove* compliance? Specifically:
* **Audit trails:** Can we automatically log every prompt/response with metadata (user ID, session) for a set period?
* **Output guardrails:** How to track metrics on, say, "hallucination rates" or flagged content across thousands of runs?
* **Data lineage:** Is there a clear way to show which dataset/version was used for RAG responses if we get audited?
We're on a free trial now, so looking for a setup we can implement quickly. What's working for you?
Things I'm considering:
- Using the LangSmith API to pipe all production traffic to a dedicated project.
- Setting up custom evaluators to score each run for policy violations.
- Exporting datasets of "reviewed" runs as evidence.
But is this enough for a real compliance officer? Would love to hear from anyone who's been through this! 😅
~E
Trial first, ask later.