When all queries funnel through a single embedding space, structurally different query types converge on the same systematic misses. Here's how to audit your retrieval diversity and fix it without blowing your latency budget.
API key scoping is not enough. When your AI agent can execute code, you need container isolation, filesystem namespacing, egress controls, and a capability audit process — or you're one prompt injection away from a lateral movement incident.
A practical decision framework for engineers deciding when to move LLM inference to the edge: latency thresholds, cost break-even analysis, the quantization quality tax, and split-inference architectures.
How to use production traffic replay to validate LLM model and prompt changes before they affect users — the infrastructure, metrics, and sampling strategies that give you confidence at a fraction of A/B test cost.
When five teams share one AI service, a single system prompt change silently breaks four evals. Here's the dependency management framework that prevents it.
Research shows AI coding assistance can lower comprehension scores by 17% and make experienced developers 19% slower while they feel 20% faster. Here's why mid-career engineers are most at risk and what to do about it.
Standard availability and error-rate SLOs don't capture behavioral quality degradation in LLM features. Here's how to define behavioral quality SLOs, set meaningful error budgets, and wire them into incident response when correctness is probabilistic.
Specification gaming isn't just an RL theory problem — it shows up in every production LLM system where incentive gradients exist. Here's how to find it and build systems that are harder to game.
Traditional SRE runbooks don't cover AI agent failure modes. Here's what actually breaks in production — infinite loops, context overflow, hallucinated API calls — and the monitoring, alerting, and cost controls that help oncall engineers respond effectively.
How SSE, WebSockets, and gRPC streaming fail differently under backpressure, what browser constraints and edge proxies break in production, and the failure-mode profile that should drive your transport choice.
Why 'pass the full conversation history' fails at p99 scale, and the session store designs, compression strategies, and operational patterns that actually hold up in production.
JSON mode guarantees your LLM output matches a schema. It does not guarantee the output makes sense. The semantic validation layer catches contradictory fields, impossible date ranges, and domain constraint violations before they silently corrupt your data.