I've been using LangSmith to debug my team's LLM chains, and it's great for us devs. But my product manager keeps asking for simple updates on what's working and what's not. They don't need to see traces or spans.
Here's what I've found works best: I create a simple shared dashboard outside of LangSmith. I take screenshots of key traces that show a clear success or a common failure pattern, paste them into a Google Doc or a Confluence page, and write one-sentence explanations next to each. For example: "Here's where the chain correctly pulled the user's order number" or "This shows it failing when the customer query has two questions."
I also export the high-level metrics (like latency or feedback scores) into a simple weekly summary slide. The key is to translate the technical "what" into the product "so what" – like "improved accuracy on this step means fewer customer support tickets." Anyone else have a smooth workflow for this?
Docs save time
I'm a lead software engineer at a mid-market SaaS company in the marketing tech space, where I manage our entire LLM ops stack, and we've been running LangSmith in production for about eight months to monitor and debug a dozen RAG and classification chains.
* **Target User & Mental Model:** LangSmith is built for developers and ML engineers, not for stakeholders. The interface assumes you understand traces, spans, and latencies. Expect to do 100% of the translation work for any non-technical consumer; it doesn't generate narrative summaries or product-ready reports natively.
* **Export and Integration Effort:** The API for pulling data is solid, but building a clean export pipeline requires work. I wrote a scheduled Python script that aggregates daily run counts, average latency per chain, and total cost from the project metrics endpoints. This takes about 3-5 hours to build initially. You cannot simply "share a view" with a PM; you must build an external dashboard in something like Grafana or Google Sheets.
* **Key Limitation: Narrative Abstraction:** Its biggest gap for your use case is the lack of a "story" layer. It shows you a failed trace, but you must manually identify the failure pattern, screenshot it, and contextualize it. For a PM, you're effectively curating a museum of bugs and wins. This manual process takes me roughly 30-45 minutes per week per PM.
* **Where It Wins for This Task:** The one built-in feature that slightly helps is the ability to tag and comment on individual traces. My team tags traces with labels like `"failure:ambiguous_intent"` or `"success:complex_query"`. I can then filter by these tags and export the list to provide counts in my weekly summary, e.g., "`ambiguous_intent` failures decreased from 15% to 8% after the prompt tweak." This provides a quantitative hook for your qualitative screenshots.
Given your described workflow of manual curation and summary slides, I'd stick with and formalize your current method. I built almost the exact same system using a Confluence template. If your PM's appetite for detail grows significantly, the next step is automating that metrics script into a live dashboard. For a clean recommendation, tell us your PM's tolerance for data freshness (is weekly manual summary enough, or do they want a live dashboard?) and whether you have any engineering bandwidth to automate the data pull.
Check the SLA.