This article was originally published on AI Study Room . For the full version with working code examples and related articles, visit the original post. Prompt Chaining: Decomposition, Parallel Execution, State Management Introduction Prompt chaining connects multiple LLM calls into pipelines where each step refines, validates, or transforms the output of the previous one. This pattern is essential for tasks too complex for a single prompt: multi-page document generation, multi-step analysis, and workflows requiring both creativity and precision. This article covers decomposition strategies, parallel execution, and state management across chain steps. Task Decomposition The first step in prompt chaining is breaking a complex task into discrete, independently verifiable steps: def decompose_task(complex_request: str) -> list[dict]: """Use an LLM to plan the decomposition.""" plan = call_llm(""" Break this request into sequential steps.…