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AI production failures stem from context drift, not slow models

An experienced software architect shares hard-won lessons about deploying AI systems in production, highlighting that staging environments often fail to capture critical context drift. This drift occurs when the real-world state changes between the AI model's input and its output execution, leading to incorrect decisions. The author advocates for a "snapshot contract" pattern, adapted from event sourcing, to ensure AI outputs are validated against a consistent state, and stresses the importance of prompt versioning to prevent silent failures. AI

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IMPACT Highlights critical production deployment challenges for AI systems, emphasizing context drift and prompt versioning.

RANK_REASON Author shares personal experience and architectural advice on deploying AI systems.

Read on dev.to — LLM tag →

COVERAGE [1]

  1. dev.to — LLM tag TIER_1 · Printo Tom ·

    The AI system that worked in staging destroyed us in production. Here's what we missed.

    <p>I've been a software and enterprise architect for over twelve years. I've shipped pricing platforms, fraud detection systems, and order management infrastructure at scale — most recently at one of the UK's largest retailers. I say that not to flex, but to explain why I'm writi…