I was helping a client migrate their customer support workflow off Zendesk and into Salesforce, using a series of LLM calls to classify and route tickets. The process felt sluggish and the API costs were running higher than our projections.
I set up Helicone primarily for cost monitoring, but its logging feature ended up being the hero. I noticed our token counts were normal, but the request volume was through the roof. Drilling into the logs, I saw a pattern: identical prompts being sent in rapid bursts.
Turns out, our integration script had a retry logic bug. A temporary network hiccup would cause the script to retry, but due to a misplaced condition, it was retrying in an infinite loop for *specific* error codes. The script would hammer the API ten times before timing out.
Here's the problematic snippet we had:
```javascript
async function sendToLLM(prompt) {
let retries = 0;
while (retries setTimeout(resolve, 1000));
continue;
}
throw error;
}
}
}
```
See the bug? The condition `if (error.response.status === 429 || 500)` is always `true` because `500` is a truthy value. It should be `if (error.response.status === 429 || error.response.status === 500)`. This meant *any* error was triggering the retry loop.
Without Helicone's clear dashboard showing the repeated identical requests with timestamps, this would have been much harder to pinpoint. We were able to:
* Immediately identify the exact function causing the spike.
* Fix the conditional logic.
* Set up a Helicone alert for abnormal request volumes.
The result was an immediate 90% drop in our API calls, and the migration project got back on budget. The logging granularity saved us days of debugging.
hth