Reliability Engineering for LLM Applications
How we reduced LLM failure rates by 65% through structured outputs, validation, and systematic testing.
Building production guardrails that keep language model features predictable and debuggable.
Deep technical breakdowns from projects where milliseconds, reliability, and maintainability matter.
How we reduced LLM failure rates by 65% through structured outputs, validation, and systematic testing.
Building production guardrails that keep language model features predictable and debuggable.
Lessons learned from deploying RAG systems for EdTech: multi-tenancy, security, and achieving >90% retrieval accuracy.
Designing a resilient retrieval-augmented generation platform for multi-tenant education workloads.
Deep dive into vectorization cost models and architecture-specific optimizations that achieved 3x performance improvements on NASA benchmarks.
Tuning LLVM vectorization and instruction selection to unlock 3× performance on real-world scientific workloads.
Technical insights from implementing AST bridging and name mangling compatibility in the Swift/Clang toolchain at Apple.
Extending the Swift compiler to seamlessly call modern C++ libraries without sacrificing safety or performance.
Every article distills battle-tested techniques from compilers, AI platforms, and reliability engineering. No fluff—just the patterns that saved releases and unlocked performance headroom.
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