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The rise of Retrieval-augmented Generation (RAG) has changed how we build large language model (LLM) applications. Instead of relying solely on a model's internal knowledge, RAG allows you to plug in your own data source, such as knowledge bases, PDF...

Background LLM applications are harder to debug and optimize than traditional software. Prompts are non-deterministic, outputs vary, and performance depends on token usage, latency, and cost. Without visibility into these dimensions, it is commercial...
