How We Built a Production LLM Pipeline in 6 Weeks
A behind-the-scenes look at architecture decisions, trade-offs, and lessons learned building a real-time LLM document processing system.
Building production LLM systems is categorically different from building demos. The gap between "it works in a notebook" and "it handles 10,000 requests a day reliably" is where most projects stall.
The System We Built
The client needed to extract structured data from 50,000 financial documents daily. We built a three-stage pipeline: document classification, field extraction using Textract + prompt chaining, and a GPT-4 validation layer.
Lessons Learned
Prompt engineering is engineering. We spent two weeks tuning prompts before touching infrastructure — accuracy went from 81% to 96% through prompt changes alone.
Fail loudly on uncertainty. Low-confidence extractions go to a human review queue rather than passing through silently.
Cost is a first-class concern. Using GPT-3.5 for validation and only escalating to GPT-4 for failures cut inference costs by 60% with negligible accuracy impact.