Hey Dev Community! I originally published a version of this breakdown over on my Medium, but I wanted to share the full technical breakdown directly with the engineers here. If you're building custom AI pipelines, this one is for you.
Every engineer building with Large Language Models (LLMs) eventually hits a hidden wall. You write a pristine system prompt, optimize your retrieval-augmented generation (RAG) code, pipeline your vector database, and push to staging. Everything looks brilliant.
Then you open your usage dashboard and look at the actual token count.
I recently hit this wall while researching how to integrate a geolocation service into our company’s core project. Like anyone trying to parse mountains of engineering guidelines, API references, and spatial data manuals, I started gathering raw documents — PDFs, Word docs, and web pages — and feeding them into Claude to fast-track my implementation research.
Out of curiosity, I paused to look closely at the underlying token consumption. What I discovered was a massive wake-up call for anyone building real-world AI applications.
