LLM Reliability

Building a production-ready LLM tool requires more than just a prompt. Discover 6 defensive engineering patterns to handle unpredictable AI outputs, manage costs, and maintain context in PDF processing.

Learn how to apply battle-tested SRE patterns like circuit breakers, failover chains, and dynamic latency tracking to build resilient LLM applications that survive provider outages.

Discover a math-heavy, judge-free method to identify Large Language Model hallucinations by analyzing semantic geometry and local consistency across multiple outputs.

Learn how to move beyond deterministic LLM outputs using Probabilistic Multi-Variant Reasoning to quantify uncertainty and improve decision-making in human-AI collaboration.

Discover how the Reflection pattern transforms unreliable LLM outputs into robust, production-grade results by enabling AI agents to verify, critique, and correct their own work.