Hey everyone! 👋 As someone who lives and breathes marketing automation and data, I've been absolutely fascinated by the move into LLM-powered applications in our field. My team has been exploring one for patient education and initial intake triage in a very controlled, compliant healthcare environment. The biggest question for us wasn't just building the chain, but how to *observe* it relentlessly.
We evaluated a few options, but LangSmith has become our central hub for observability. The stakes are so high with healthcare data—hallucinations aren't just inconvenient, they can be dangerous. So we needed granularity. Here’s a breakdown of why it’s working for us, framed around our needs:
* **Trace Visualization for Complex Workflows:** Our agent doesn’t just call an LLM. It checks a patient query against a knowledge base, decides if it needs to retrieve more data, and then formats a safe, cited response. LangSmith's trace view lets us see the entire decision tree in one timeline. When a response felt "off," we could pinpoint it to a specific retrieval step that pulled an outdated document, which was a huge win for accuracy.
* **Dataset Management for Continuous Testing:** We maintain a growing dataset of anonymized sample patient questions and ideal, vetted responses. Before any model swap or prompt adjustment, we run an evaluation run in LangSmith. It compares outputs against our golden dataset for correctness, but also for things like citation presence and tone (we have a custom checker for "anxiety-inducing language"). This is like A/B testing on steroids for our core logic.
* **Feedback Integration & Alerting:** We have a simple thumbs-up/thumbs-down button in our app's UI. This feedback is piped back into LangSmith as a dataset. A cluster of "thumbs down" on similar traces triggers an alert for our team. This is eerily similar to lead scoring—we're scoring "risk" instead of "interest," and prioritizing issues that could impact patient trust.
* **PII & Compliance Considerations:** While LangSmith doesn't automatically redact PII, its annotation features allowed us to tag and filter out any trace that handled sensitive data during our internal debugging. We have strict protocols to never send real PHI through, but for development with synthetic data, this helps maintain our audit trail.
The main pitfall to watch for is cost at scale. Every trace is stored, and with high user volumes, it can add up. We had to be methodical about sampling in production (we log 100% in staging) and set up clear retention policies.
Has anyone else implemented LangSmith in a similarly regulated field? I'm particularly curious about how you've structured custom evaluators for safety or compliance checks. Our "non-anxious tone" checker is a start, but I'd love to compare notes on building more sophisticated guardrails!
test everything twice