Show HN: Common Pitfalls in Production-Ready AI Systems

🚀 Unlocking Productivity in AI Infrastructure

Over the past two years, I’ve developed and debugged numerous production pipelines, uncovering a crucial pattern: most failures aren’t immediate crashes but silent faults compromising system relevance, accuracy, and stability. To combat this, I created a public reference detailing 16 distinct failure modes encountered in real-world scenarios, including:

• Semantic drift after chunking
• Embedding/meaning mismatches
• Cross-session memory gaps
• Recursion traps

Why share this?

• Common Vocabulary: Naming failures speeds up discussions and resolutions.
• Early Detection: Teams can vet new features against these modes before deployment.
• Community Collaboration: Your insights can refine this resource, preventing future incidents.

This reference has already helped startups and fueled my projects to avoid hours of trial-and-error.

✨ Join the conversation! Check out the full table here and share your experiences or insights!

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