Develop Production LLM Applications

Building an LLM application that users can trust in production is fundamentally different from building one that works in a demo. The demo works with carefully chosen inputs and a forgiving evaluator. The production system faces adversarial inputs, edge cases, model hallucinations, rate limits, and cost management — none of which appear in tutorials. This plan is built around production realities, not demonstration scenarios.

The session covers LLM framework selection with their specific tradeoffs, building a Retrieval-Augmented Generation system that grounds the model in your data rather than relying on training data, implementing agent patterns for multi-step reasoning, adding safety layers and output validation, and deploying with monitoring. The RAG architecture is the most important concept: it's what makes LLM applications factually reliable by providing relevant context at inference time.

Model drift and data quality are the two production problems most LLM tutorials don't mention and most production systems eventually face. Model providers update models without always announcing behavioral changes, and your data distribution changes over time — both degrade application performance invisibly. The monitoring instrumentation built in this plan catches both by logging inputs, outputs, and evaluation scores. The difference between an application that degrades quietly and one that alerts you before users notice is whether you built monitoring before launch or planned to add it later.