What Is LLMOps, and Why Does It Exist?
Explore why LLMOps emerged as a discipline, what fundamentally breaks when large language models move from prototypes to production, and how the 4D life cycle provides a structured approach to building and operating reliable LLM-powered systems.
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Large language models are now being embedded in customer-facing products, internal workflows, and core business systems.
As organizations integrate LLMs into production systems, a common pattern appears: models are easy to prototype but difficult to run reliably at scale. This gap exists because LLMs behave differently from traditional software systems when exposed to production users, data, and traffic.
LLMOps is the discipline focused on managing this gap. To make this concrete, consider the following scenario.
On a Friday afternoon, you discover a new LLM framework and write a small Python script to build a policy Q&A bot over your company’s HR documents. You run it locally, and it works as expected. It answers questions, cites the employee handbook, and returns readable responses. You commit the code and move on. On Monday morning, the bot gets deployed to the company’s Slack workspace.
Within an hour, three things happen:
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