Comment by jpalepu33

This metaphor really captures the current state well. As someone building products with LLMs, the "you have to tell it where to turn" part resonates deeply.

I've found that the key is treating AI like a junior developer who's really fast but needs extremely clear instructions. The same way you'd never tell a junior dev "just build the feature" - you need to:

1. Break down the task into atomic steps 2. Provide explicit examples of expected output 3. Set up validation/testing for every response 4. Have fallback strategies when it inevitably goes off-road

The real productivity gains come when you build proper scaffolding around the "horse" - prompt templates, output validators, retry logic, human-in-the-loop for edge cases. Without that infrastructure, you're just hoping the horse stays on the path.

The "it eats a lot" point is also critical and often overlooked when people calculate ROI. API costs can spiral quickly if you're not careful about prompt engineering and caching strategies.