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: Building Multi-Step LLM Workflows Prompt chaining connects multiple LLM calls to accomplish complex tasks. Instead of solving everything in one prompt, chains break the task into manageable steps. Each step builds on the previous output. Chain Types Sequential chains pass output from one step to the next. Example: extract text → summarize → translate. Each step depends on the previous one. Sequential chains are simple but accumulate latency. Parallel chains execute independent steps concurrently. Example: research a topic from multiple sources simultaneously. Parallel chains reduce total execution time for independent sub-tasks. Conditional chains use the output of one step to decide what to do next. Example: classify the user intent, then route to the appropriate handler. Conditional chains enable flexible, adaptive workflows.…