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LLM-as-a-judge evals look reliable in the test harness. Here's what breaks after months in production: calibration drift, noisy decision boundaries, cascade failures in multi-step pipelines, and the meta-evaluation trap.
Constrained decoding eliminated JSON syntax failures in LLM structured output. The reliability problem has moved to semantics: four failure classes that valid JSON hides, and the runtime patterns that catch them.
MIT's Project NANDA analysed 300 enterprise AI deployments and found 95% delivered no measurable P&L impact. The reason is almost never the model. It's task structure.
Vendor benchmarks leave out the two cost items that usually flip the self-hosting decision: engineering overhead and the model-update cycle. Here is the honest break-even analysis.
Chroma's 2025 research tested 18 frontier models and found every one degrades as context grows. This is what context rot means for production engineering decisions — and the specific patterns that address it.
AI is making specific tasks measurably faster: coding 55%, X-ray reading 36%, customer service sales up 16%. And yet 90% of firms saw no firm-level productivity gain. Here's what the gap means.
MCP is becoming the standard interface for connecting AI agents to external tools. But most teams adopting it don't have a clear picture of what the protocol covers and what it deliberately leaves out.
Three years into the AI product era, wrapper company failure rates are well-documented. Less examined is why intelligent founders keep building them anyway — and the one condition that makes some of them right.
RAG pipelines fail for a reason most teams never investigate: the chunks are structurally broken. Here's how to diagnose the actual failure mode before changing anything else.
Prompt caching keys on the leading token prefix. One dynamic field early in the prompt invalidates the cache for everything after it. Here is what that means for how you structure production prompts.
AI agents consume compute in ways that don't map to user count — and Copilot's June 2026 billing shift is the clearest signal yet. Here's what the transition reveals about pricing for AI-powered products.
LLM features fail slowly, partially, and semantically — not with clean error codes. Designing for this requires different patterns from the distributed systems toolkit you already know.
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