Reliability-first LLM systems

A practical blueprint for production GenAI with evals and fallbacks.

Draft

Topics

ReliabilityEvalsFallbacksMonitoring

Production LLMs fail at the edges - not in demos. This piece maps the failure modes and the patterns I use to ship reliable GenAI systems.

Outline

Where LLM systems fail in production
Layered reliability patterns that actually work
Eval loops that prove progress
Operational checklists for shipping

Failure map

Provider throttling and partial outages.
Prompt drift and hidden context changes.
Tool failures that look like model issues.

Reliability layer

Retries, fallbacks, and routing rules.
Strict output contracts with validation.
Budgets for latency, cost, and tokens.

Eval loops

Golden sets that mirror real usage.
Prompt regression tests in CI.
Monitoring for accuracy and drift.

Operational checklist

Tracing across model + tools.
Error budgets and escalation paths.
Release gates before shipping changes.

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