Why I Modelled My LLM Pipeline as a DAG Instead of a Chain — and What I Found Out

The problem with chains in production Every major Python LLM framework gives you the same primitive: a chain. LangChain's LCEL. LlamaIndex's pipeline. Haystack's components. They all model your pipeline as a linear sequence of steps — input flows through A, then B, then C, output comes out the end. For a hello-world RAG demo, that's fine. For a production system, you hit the wall fast. What chains can't express cleanly Here's a real pipeline I needed to build: Classify the inco...