Workflows are essential in guiding a series of tasks or steps that need to be completed in a specific order to reach a particular goal. In the world of LangGraph, workflows represent an automated, structured way to handle complex tasks efficiently. Below are the key types of LLM workflows used to tackle various tasks: 1. Prompt Chaining 🔗 In Prompt Chaining, multiple interactions or calls to the model are made in a sequence. The process involves providing a topic initially, and then engaging in multiple steps with the model to gradually build a complex output.Example: A user provides a topic, and the model generates a report step-by-step by chaining different tasks. 📑💬 2. Routing 🔄 Routing is about understanding a task and deciding which agent or system will execute it. For instance, a customer support query can be routed to an LLM, which then analyzes the task and decides how to handle the query.Example: A customer sends a query, and the system routes it to the appropriate model based on the nature of the query. 🧑‍💼🔍 3. Parallelization ⚡ Parallelization involves breaking a task into multiple sub-tasks that can be executed simultaneously, allowing for faster and more efficient processing. Once all sub-tasks are completed, the results are merged to produce the final outcome.Example: A large report can be divided into multiple sections, each section processed concurrently, and then combined to form the complete report. 📊📝 4. Orchestrator-Worker Workflow 🎶 This workflow is similar to Parallelization, but with an important difference: the nature of the sub-tasks is unknown. The orchestrator divides the main task into parallel sub-tasks and orchestrates them without knowing the exact nature of those sub-tasks.Example: The orchestrator assigns sub-tasks to workers and ensures everything is completed in parallel, but the sub-tasks may differ based on how the system operates. 🔄🤖 5. Evaluator-Optimizer Workflow 🧠⚙️ In this workflow, a task may be difficult to execute perfectly at once. The goal is to evaluate the task, find its weak points, and optimize the process in steps, improving the result incrementally.Example: A complicated design might be generated by improving it through multiple iterations, refining and optimizing each step. ✏️🔧

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