Скачать или смотреть You Know Exactly What You Want. AI Has No Idea.

AI output drift doesn't happen because something changed—it happens because AI never had access to the decisions that would have kept output stable. The inconsistency you're experiencing has a structural cause, not a random one.

When a prompt that worked last week produces different results this week, the obvious explanations—prompt quality, model variability, your own memory—don't fully resolve it. AI executes against whatever you provide in a given session. The decisions that shaped your expectations exist as your felt sense of rightness, not as externalized criteria AI can access. Your memory of what you wanted reconstructs each time, so the instruction set shifts between sessions even when you believe you're asking for the same thing.

Partial documentation creates false confidence when standards exist but never reach AI's input. Documentation itself can fail through premature commitment, vague criteria, or over-rigidity. What AI needs is a stable decision foundation—externalized standards concrete enough to execute against. When that exists, output arrives aligned before review, corrections become permanent, and drift relocates from output-level symptoms to decision-level governance.

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CHAPTER MARKERS
00:00 Why AI Output Drifts Without Warning
01:03 Why AI Can Only Execute What It Receives
04:57 Why Blaming the Prompt, Model, or Memory Doesn't Work
08:54 Why Understanding the Problem Doesn't Fix It
13:08 How Documentation Can Fail
17:34 What AI Actually Needs to Stay Aligned
20:52 What Changes When Decisions Persist
24:36 Where Drift Relocates
27:32 What Separates Systems That Drift From Systems That Hold