  LLM chains enable complex tasks by decomposing work into a sequence of
subtasks. Similarly, the more established techniques of crowdsourcing workflows
decompose complex tasks into smaller tasks for human crowdworkers. Chains
address LLM errors analogously to the way crowdsourcing workflows address human
error. To characterize opportunities for LLM chaining, we survey 107 papers
across the crowdsourcing and chaining literature to construct a design space
for chain development. The design space covers a designer's objectives and the
tactics used to build workflows. We then surface strategies that mediate how
workflows use tactics to achieve objectives. To explore how techniques from
crowdsourcing may apply to chaining, we adapt crowdsourcing workflows to
implement LLM chains across three case studies: creating a taxonomy, shortening
text, and writing a short story. From the design space and our case studies, we
identify takeaways for effective chain design and raise implications for future
research and development.
