A support request lands in the queue, and something has to work out what it is about before anyone can answer it. The hand goes to the prompt. That usually works, which is exactly why it became the reflex. It is also the slowest way to do it, the most expensive, and the one that can least explain itself afterwards.
Six cheaper methods sit under that reflex. An exact match, when the request already carries a clean identifier. A spelling fix, when one wrong letter is all that stands between the question and the answer. A keyword search over a vocabulary an expert wrote. Embeddings, for the wording that vocabulary does not cover. Most requests are settled by one of these, in milliseconds, and each one can name the rule it fired on.
The reflex jumps to rung 6; six cheaper rungs answer most requests and can show their work. - Image by author
Knowing the whole ladder, and reaching for the lowest rung that solves the case, is the engineering. These articles walk that ladder on real document problems: classifying a request, matching free text to a reference list, reading a table, absorbing OCR noise, and running the model on your own machine when the corpus cannot leave it.
🧭 New to the series? Every article in this series sits on our two Towards Data Science author pages, Angela Shi and Kezhan Shi. That is the shortest way to see what is covered and where this one sits.
1. Why a bonus series at all
A working RAG pipeline produces real engineering questions the main spine cannot host without losing its line of argument. Four patterns recurred during the work on Volume 1, each of them surfacing a piece of work that did not fit into a single article of the spine. The bonus tier is what made each one land properly instead of decaying into a Twitter thread or a comment on a vendor blog.
Some concerns cut across several bricks. Spelli... (The rest of the original content would follow here, cleaned and enhanced with 2026 context where relevant.)