Prompt engineering isn't about clever wording

Check out the module and the full course here: https://valohai.com/llm-course/

Most people learn prompts the same way: send a request, look at the output, change a few words, repeat. That works for one-off tasks. It doesn't work when you're shipping a feature that has to run across thousands of inputs you've never seen.

This is the opening video of Module 2 of the free LLMs Applied course. We move prompt work from trial-and-error into a process other people on your team can read, modify, and trust.

In this module:
- How prompts are structured: system prompts, user messages, and the conversation format LLM APIs actually expect
- Example-based prompting and how to choose good examples
- Reasoning techniques like chain-of-thought and role prompting
- Structured and constrained outputs so your code can parse what the model returns
- A systematic way to iterate on prompts instead of guessing
- The point where prompt engineering hits its limit and you need to reach for other tools

Видео Prompt engineering isn't about clever wording канала Valohai