Hey everyone. Still pretty new here, but I had a bit of a breakthrough this week thanks to LangSmith, and I figured my migration pain might help someone else.
We’ve been building a RAG pipeline for our sales team’s product docs. After the initial setup, things seemed okay—answers were decent, but nothing spectacular. We ran a batch of 10k test queries through LangSmith to really see where we stood. The performance metrics were... humbling, honestly. But the real gold was in the trace visualization.
LangSmith clearly showed that for a huge chunk of queries, the system was retrieving 4-5 document snippets, but only *one* of them was ever being used to form the final answer. The rest were just... there. It was like watching our retrieval work for nothing.
Coming from a Salesforce background where debugging is often a black box, this visual trace was a revelation. It pointed directly to our chunking strategy. We were using a simple fixed-size chunker, and it was obviously splitting key concepts across boundaries. The system would fetch a chunk with half an idea, then another chunk with the other half, but the LLM couldn’t synthesize them effectively from the separate contexts.
We switched to a semantic/recursive chunking approach, paying attention to natural topic boundaries (like headings). After re-ingesting and running another test batch, the difference in the traces was night and day. More retrieved chunks were actually utilized, and answer quality scores jumped.
The lesson for me, especially after my messy HubSpot migration, is that you can’t guess at this stuff. You need that objective, visual feedback on what’s *actually* happening in your chain. LangSmith gave us the concrete evidence we needed to stop tweaking prompts blindly and fix the real problem: our data foundation.
Always backup first