From Concept to Production with Observability in LLM Applications

Understanding observability in AI applications, particularly in Large Language Models (LLMs), is crucial. It’s all about tracking how your model performs over time, which is especially challenging with text generation outputs. Unlike categorical outputs, text generation can vary widely, making it essential to monitor the behavior and performance of your model closely.

Imagine you’re developing an application tailored to a specific use case. Perhaps you’re enhancing an LLM with an external corpus through techniques like RAG (Retrieval-Augmented …

ai ai applications applications behavior categorical concept language language models large language large language models llm llm applications llms making observability performance production software development text text generation tracking understanding