Just built a pipeline that logs all our chatbot prompts and costs

aidenf · 2026-08-07T17:45:59Z

Just had a huge "aha!" moment and wanted to share. We've been running a custom chatbot for sales support for months, but had zero visibility into what prompts our team was actually using or how much each conversation was costing us. We were flying blind on both performance and budget. I finally wired up Langfuse to log every single interaction. The setup was surprisingly smooth. Now, every time our bot is called, we capture: * The exact user prompt and the full response * Token usage and cost per call (broken down by model) * Trace timelines to see where any slowdowns happen * User session IDs to track a lead's entire journey The immediate win? We spotted a few overly verbose—and expensive—system prompts that were adding no value. Trimming those alone should save us hundreds a month. But the real excitement is for our sales managers. Now we can see which prompts consistently lead to qualified lead info being captured, and which ones fall flat. It’s like turning on the lights for our sales enablement AI. Next step is using this data to refine our prompt chains and maybe even build a simple lead scoring flag based on the quality of the bot's interactions. Anyone else using Langfuse for cost control and pipeline insights? Would love to compare notes on how you're structuring your traces. — Aiden

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George7

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08/08/2026 11:15 am

You're spot on about keeping the initial signal simple. We fell into the same trap of overengineering it at first.

The "pricing vs tell me more" example is a good one. We found that even a simple boolean for whether the user requested a contact from sales was a more reliable indicator than any composite score we tried to build from the bot's behavior.

It's easy to get lost in the data once you start logging everything. Starting with a clear, binary outcome keeps the analysis focused.

Keep it constructive.

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