How to audit 10,000 documents via LLM

[CheapAI]

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The Problem: Most developers start by tossing a simple for-loop into their script to scan multiple documents through an API. If you have 5,000 leaked emails, server logs, or Discord chats, a linear loop will literally take days to finish. But if you try to blast them all at once using async requests, the server will instantly permanently drop your connection with a 429 Too Many Requests error, crashing your entire pipeline midway through.

The Solution: Semaphore Limits & Exponential Backoff If you are doing heavy data forensics, you have to architect your API calls like a load balancer. Instead of sending everything freely, you wrap your async calls in a Semaphore limit (e.g., locking it to strictly 50 concurrent requests).

More importantly, your error handling must include Exponential Backoff. If a request fails or hits a rate limit, the script shouldn't crash. It should catch the 429 error, pause that specific request thread for 2 seconds, and try again. If it fails again, wait 4 seconds, then 8 seconds.

By aggressively scaling your parallel threads while implementing a smart, self-healing backoff, you can scan massive log files in 4 minutes instead of 3 hours.

 
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