Flow engineering in the context of large language models (LLMs) is a methodology designed to optimize how these models handle tasks by implementing a structured, multi-step process. Unlike [prompt engineering](/glossary/prompt-engineering), which relies on crafting a precise single prompt to elicit a desired response, flow engineering involves breaking down the problem into smaller components and generating diverse test cases to cover various scenarios. This allows for a thorough analysis and a more comprehensive approach to problem-solving.

The iterative refinement process in flow engineering sets it apart. The model generates initial solutions, tests them against predefined cases, identifies errors, and makes necessary adjustments. This loop continues until the model produces a robust solution. This method ensures higher accuracy and reliability, especially for complex tasks like code generation, where multiple iterations and refinements are crucial.

In contrast, prompt engineering focuses on finding the right input to achieve the desired output in a single step. While effective for simpler tasks, it often falls short in handling nuanced and complex scenarios that benefit from an iterative process. Prompt engineering's reliance on one-off prompts can lead to limitations in producing high-quality results for intricate problems.

Flow engineering also contrasts with [agentic workflows](/glossary/agentic-workflows), where models operate as autonomous agents capable of making decisions and performing actions independently. While agentic workflows excel in dynamic decision-making and real-time interactions, flow engineering is ideal for tasks that require meticulous, step-by-step refinement. By combining the strengths of iterative refinement from flow engineering with the precision of prompt engineering and the autonomy of agentic workflows, developers can harness the full potential of LLMs across a wide range of applications.