Hi everyone! I'm pretty new to the whole data pipeline world, and I've been trying to learn about tools for managing LLM applications. At my company (a small fintech startup), we're starting to explore using LLMs for some basic tasks, like summarizing customer inquiries or helping draft standard reports.
I keep hearing about LangSmith as a platform for evaluation and monitoring. Since we're in finance, compliance is a huge deal for us. We can't just deploy a model without being able to audit its outputs and ensure it's not hallucinating numbers or giving non-compliant advice.
My question is: for those of you in finance or similarly regulated industries, is LangSmith the top choice for prompt evaluation? I'm a bit overwhelmed trying to figure out if it's built for this kind of high-stakes environment.
Specifically, I'm curious about:
- How easy is it to set up rigorous test suites for things like fact-checking against our internal data or checking for prohibited language?
- Can it integrate well with our existing data stack (we use BigQuery for data and Airflow for orchestration)?
- Does it provide the kind of audit trails that would satisfy a compliance officer?
I've been reading the docs, but real-world experience would be super helpful. Are there other platforms you considered that were better suited for compliance needs? Thanks in advance for any guidance! 😅
LangSmith isn't built for compliance out of the box. It's a dev toolkit. For your use case, you're looking at a custom pipeline.
>audit trails that would satisfy a compliance officer
You won't get that from any single platform. You need to build it. Your pipeline needs to log every input, output, and the exact context retrieved to somewhere immutable. We pipe everything to S3, then use Athena to query. Forbidden language checks are just regex or classifier calls you script yourself.
Integration is on you. Airflow can call the LangSmith API, and you can write results to BigQuery. But the "rigorous test suites" for fact-checking? That's your own code validating against your data, not a LangSmith feature.
Consider it a component, not the solution.
Metrics don't lie.
That makes sense, treating it as a component. It reminds me of setting up analytics tracking where you build the audit layer separately.
What format do you use for the logs in S3? Is it structured JSON that your compliance team can read directly, or does it need another transformation step?
You've hit on the real challenge here! User634 is spot on that LangSmith is a component. For your fact-checking and prohibited language tests, you'll be building those suites yourself in Python. The value is that LangSmith gives you a structured place to run and track those custom evaluations across prompt versions.
On your specific point about audit trails for a compliance officer: you can export data from LangSmith, but the immutability and full data lineage they'll demand has to be your pipeline's job. What I did was create a standardized logging schema that captures everything - the prompt template ID, the exact retrieval context, the raw output, and the results of our custom compliance checks - then send that as a JSON log to BigQuery *and* to a secured storage bucket. LangSmith becomes your development and testing layer, while your production pipeline builds the real audit log.
Could you share a bit more about the "prohibited language" you need to catch? Is it regulatory keywords, or more nuanced advice? The approach differs a lot.
Measure twice, automate once.
The existing answers correctly frame LangSmith as a component, but I'd add a critical nuance for your compliance context. Its core value isn't in providing audit trails, but in enabling systematic, version-controlled prompt testing *before* deployment.
For your questions:
- The "rigorous test suites" are indeed custom Python evaluators you write. However, LangSmith's environment lets you run these suites across hundreds of prompt variants against a frozen dataset, which is essential for proving due diligence in development. You can test fact-checking by validating outputs against a BigQuery snapshot you load into the test run.
- Integration is manual. You'd orchestrate model calls and evaluations via Airflow, using the LangSmith SDK to log traces and results. The trace data then needs to be extracted and merged with your broader pipeline logs in BigQuery to establish a complete lineage.
- The platform's native audit trail is insufficient for a compliance officer. It shows the 'what' of a test run, not the 'why' behind a production decision. You must design a logging schema that includes business context, user IDs, and approval states, then pipe the LangSmith output into that schema as a single event source.
So, it's a top choice for the evaluation phase, not the operational compliance phase. Your risk assessment should treat it as a specialized development tool that feeds into a separate, immutable logging system you control.
Migrate slow, validate fast.
Good answers already. They're right about the component part, but let's talk about the real cost that no one's mentioning.
LangSmith itself is another line item on your cloud bill. You're going to be running your own evaluations and logging everything to BigQuery and S3 anyway for that compliance audit trail, as noted. So before you buy the platform, figure out what it's actually saving you versus building the versioning and test orchestration into your existing Airflow DAGs. The "systematic testing" feature has value, but you need to see the break-even point on engineer hours to build a basic alternative versus the subscription cost.
For a small fintech, my main question is: have you quantified your expected monthly LLM inference volume? That's going to dictate whether you need a fancy eval platform or just a few well-instrumented scripts. The compliance officer doesn't care *what* tool you used, only that you can pull a complete, immutable record for any given output.
Show me the bill