The advice in 2026 is settled: chunk your documents, embed them, retrieve top-k, feed those to the model. Don't waste context. Don't waste tokens.
I built a system that deliberately does none of that. Every query gets the expert's entire CV — 69 projects, publications, education, languages. Roughly 34,000 characters, in full, every time.
Here's the reasoning, because it wasn't laziness.
The task decides the architecture
The system finds UN tenders for one specific expert. Every day it crawls 28 sources, normalises and deduplicates thousands of procurement notices, then answers one question per tender: does this person fit?
