What Is LLMOps, and Why Does It Exist?
Learn to identify why large language models behave differently in production and discover the operational strategies of LLMOps that ensure reliability, cost-effectiveness, and user satisfaction. This lesson introduces the 4D LLMOps life cycle providing a framework for designing, deploying, and maintaining LLM-powered systems under real-world constraints.
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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. ...