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Adding a self-correction loop to an LLM pipeline for structured data extraction reduced consistency from 85% to 62%, due to compounding noise and regeneration drift. The article discusses potential solutions like granular diff mechanisms or deterministic gates.
Gergely Orosz notes that using more context in an AI agent's context window over longer runs can increase errors and drift, suggesting shorter runs with less context for better reliability.