Flow engineering, AI workflows, and intelligent automation strategies for developers and AI builders. In this episode of Tool Use, we explore how orchestrating multiple AI steps transforms complex systems beyond simple prompt engineering.
Learn why most AI builders focus too much on crafting perfect prompts when real-world applications demand intelligent workflows. Discover Minki Jung's practical advice on decomposing tasks, designing ergonomic prompts, and building AI systems that actually work.
Key topics covered:
- How to think about AI workflows like Henry Ford's assembly line
- The difference between prompt engineering and flow engineering
- Practical techniques for decomposing complex AI tasks
- When to use LLMs vs. traditional programming in your workflow
- How to create memory and judgment within AI systems
- Tips for testing and evaluating AI workflows
Whether you're building AI content systems, tools, assistants, or decision engines, this episode will completely change how you approach AI architecture.
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00:00:00 intro
00:02:20 - What is Flow Engineering?
00:03:41 - Workflows vs Autonomous Agents
00:06:46 - The Henry Ford Approach to Workflows
00:10:26 - How to Decompose AI Tasks (7 Steps)
00:16:38 - Choosing the Right LLM for Each Step
00:21:44 - Pro Tips for Prompt Engineering
00:27:46 - Learning AI & Avoiding Hype (Focus!)
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