Prompt Chaining: Turn One Prompt Into a Reliable LLM Workflow

Complex tasks overwhelm large language models when crammed into single prompts, causing them to skip steps, forget constraints, and produce unreliable outputs that are impossible to debug. Prompt chaining solves this by breaking big tasks into smaller, independent sub-tasks with dedicated prompts for each step, where structured outputs from one stage become inputs for the next, creating reliable workflows that can be controlled and debugged station-by-station.