Prompt chaining, e.g. generating a document and then translating it to a separate language as a second LLM call
Routing, where an initial LLM call decides which model or call should be used next (sending easy tasks to Haiku and harder tasks to Sonnet, for example)
Parallelization, where a task is broken up and run in parallel (e.g. image-to-text on multiple document pages at once) or processed by some kind of voting mechanism
Orchestrator-workers, where a orchestrator triggers multiple LLM calls that are then synthesized together, for example running searches against multiple sources and combining the results
Evaluator-optimizer, where one model checks the work of another in a loop