How to Turn Your LLM Prototype into a Production-Ready System

Moving a large language model (LLM) from a simple prototype to a production-ready system requires a structured methodology beyond initial 'wow-effect' applications. This process involves crucial steps like optimizing prompts, enforcing structured outputs using schemas, and enabling the model to call external tools for richer context and reduced hallucinations. Furthermore, implementing guardrails for input/output validation and defining business rules are essential for reliability and safety. The guide uses the practical example of building an automated grading system for data science exams to illustrate how to combine these elements into a testable and maintainable pipeline.