Analysis of 1,200 production LLM deployments reveals six key patterns separating successful teams from those stuck in demo mode: context engineering over prompt engineering, infrastructure-based guardrails, rigorous evaluation practices, and the recognition that software engineering fundamentals—not frontier models—remain the primary predictor of success.
Analysis of 1,200+ production LLM deployments reveals that context engineering, architectural guardrails, and traditional software engineering skills—not frontier models or prompt tricks—separate teams shipping reliable AI systems from those stuck in demo purgatory.
Lessons from the Maven Evals course are combined with 50+ real-world case studies from ZenML's LLMOps Database to show how companies like Discord, GitHub, and Coursera implement the Three Gulfs model and Analyze-Measure-Improve lifecycle to transform failing LLM systems into production-ready applications.
287 latest curated summaries of LLMOps use cases in industry, from tech to healthcare to finance and more. This blog also highlights some of the trends observed across the case studies.
A comprehensive overview of lessons learned from the world's largest database of LLMOps case studies (457 entries as of January 2025), examining how companies implement and deploy LLMs in production. Through nine thematic blog posts covering everything from RAG implementations to security concerns, this article synthesizes key patterns and anti-patterns in production GenAI deployments, offering practical insights for technical teams building LLM-powered applications.
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