Why your RAG pipeline answers wrong (and it is rarely the chunking)
Teams spend months tuning chunk sizes while the real loss happens one step later. A short guide to finding the stage that is actually costing you accuracy.
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Short, specific write-ups on the problems that actually show up when AI features meet real traffic. No roundups, no hype.
Teams spend months tuning chunk sizes while the real loss happens one step later. A short guide to finding the stage that is actually costing you accuracy.
The cost per request is set long before it reaches your billing dashboard. It is decided in the product spec, by people who have never seen a token count.
Most unreliable agents are not under-prompted. They are over-optioned. The fix is usually to delete tools, not to add reasoning.
Forty examples beat any amount of prompt intuition. Here is how to build a set small enough to actually write and useful enough to catch regressions.
Model migrations fail in the same four places every time. None of them are the ones people plan for.