MythyaVerse Journal
Engineering notes for teams shipping AI into real-world messiness.
We write about the hard part of applied AI: making systems survive ambiguity, scale, and production expectations. Every article here is built from problems that show up after the demo.
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A demo pipeline looks linear and predictable. Production traffic introduces ambiguity, follow-ups, mixed language, and noisy inputs that force the system off the happy path.
Production AI
18 Hidden Mistakes That Keep Your RAG System Stuck in Demo Mode
A practical breakdown of the 18 real failure modes that show up when a simple Retrieval-Augmented Generation system meets production traffic, plus the architecture patterns required to fix them.
April 22, 202612 min readMythyaVerse AI Engineering Team
RAGLLM SystemsAI ArchitectureProduction Engineering
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