Author here. I work at ZenML, where we maintain the LLMOps Database (https://www.zenml.io/llmops-database) — a collection of production LLM case studies we've been cataloguing for a while now. This post summarises patterns from the latest 400+ entries (database just crossed 1,200 total).
The findings that surprised me most:
- Context engineering has become a distinct discipline — teams are treating the million-token window as a ceiling to stay under, not a feature to exploit
- Software engineering skills matter more than AI expertise for production success (durable execution, distributed systems, infrastructure work)
- The "wait for the next model" strategy keeps not working — teams shipping today are constraining models, not unleashing them
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Author here. I work at ZenML, where we maintain the LLMOps Database (https://www.zenml.io/llmops-database) — a collection of production LLM case studies we've been cataloguing for a while now. This post summarises patterns from the latest 400+ entries (database just crossed 1,200 total).
The findings that surprised me most:
- Context engineering has become a distinct discipline — teams are treating the million-token window as a ceiling to stay under, not a feature to exploit
- Software engineering skills matter more than AI expertise for production success (durable execution, distributed systems, infrastructure work)
- The "wait for the next model" strategy keeps not working — teams shipping today are constraining models, not unleashing them
There's also a shorter TL;DR version if you prefer: https://www.zenml.io/blog/the-experimentation-phase-is-over-...