Your English eval suite cost $40K. The seven-locale international launch will not cost $280K — the real curve is closer to N×L^1.3 because cross-locale comparison is a meta-eval that doesn't decompose.
Shared on-call rotations break the moment one of those services is an LLM-backed feature. Here is the literacy prerequisite, dashboard hygiene, and shadow-period playbook that keeps the AI team out of bed at 2am.
Shipping one on-device model to every user means you're either burning battery on flagships or shipping a degraded product on the long tail. The discipline that fixes it looks more like a CDN than a model registry.
Tools that return unbounded lists turn agents into the SELECT * antipattern of the function-calling era. Pagination is a load-shedding primitive — make it a convention in your tool catalog, not a per-tool decision.
Vector stores ship without the migration tooling Postgres has had for two decades — no ALTER TABLE, no online schema change, no per-row version. The discipline that makes embedding upgrades survivable starts with a single column most teams forget to add.
Prompt caching's discount is real until one tenant's launch evicts everyone else's prefixes. The shared inference cache is a tenant-coupling surface, and the bill lands weeks after the incident.
A four-word edit to a system prompt can break parsers, judges, and chained agents that pinned the old wording. Prompts are APIs with silent consumers — and the discipline that keeps them stable looks a lot like REST endpoint deprecation.
Behavioral evals catch what your model says; they cannot catch what your prompt is. A prompt linter — fast, deterministic, structural — closes the gap that ships eval-green and surfaces as a 11pm production incident.
When a system prompt grows past 2K tokens, position bias makes a moved instruction as load-bearing as a rewritten one — and line-based diffs hide it. How three teams silently overwrite each other's intent, and the section-ownership and eval discipline that catches it.
The 'representative customer' you pasted into your few-shot prompt six months ago is still in production — re-identifiable, re-shipped, and invisible to DLP.
Layered safety pipelines silently triple p95 latency and cost on the long tail. Treat guardrails as a budgeted resource with tiered classifiers, parallel checks, and an honest latency contract.
Most RAG pipelines run two models in series — retriever and reranker — but eval suites only grade the generator's output. When the reranker drifts, the dashboard shows answer quality dropping with no causal arrow. Here's how to build a reranker eval that catches the silent regressions.