LLM Workflow Patterns
Let's see five key patterns in which LLM Workflows are implemented. Prompt Chaining This workflow is used when we can break down a complex task into a sequence of steps, and each step can be processed as individual LLM call using the output of the previous step. Example: Multistep reasoning tasks such as summarize text, critique and then rewrite Code generation pipeline such as generate plan, write code, test code, explain output Router This workflow is used in cases where an agent classifies the intent/task type/domain of the input query and then delegates the request to another agent, tool or workflow. Example: Multimodal agents such as those with separate text processing and image processing LLMs Multidomain assistants such as processing questions related to legal vs finance vs medical domains Parallelization The workflow pattern is useful when a task can be broken down into subtasks which can be handled concurrently by multiple LLM or agents. The outputs of these multiple call...