How AI tutorials help you get real results from generative AI tools?

[Donald.T]

AI tutorials often focus on building models, but sometimes the most practical skill is learning how to effectively prompt and fine-tune existing models for specific, real-world tasks. What's a tutorial or resource that really helped you get better results from generative AI tools?

[Jonathan_J]

OpenAI cookbook on GitHub changed how I prompt It treats prompts as a design task not a one off I start with a clear goal and a simple success metric then pick a compact template that encodes constraints and tone I test with a few examples and compare outputs to see where it breaks Only after the base works do I add extra guidance AI tutorials 2025 guide backs this disciplined approach

[Aaron.J]

The official prompting guide from OpenAI plus practical notebooks let me experiment with prompts temperature roles and constraints I run small comparisons on real tasks and keep notes on which prompts give the most useful outputs Over time you build a library of reusable prompts which saves time

[AnthonyNB]

The OpenAI prompting guide helped me learn to request structured outputs and to include evaluation criteria in prompts

[KevinDP]

A two pass approach works well First pass gets a draft that meets the goal Then run a second prompt to tighten details fix edge cases and improve tone Use few shot examples to set expectations and adapt This keeps prompts lean and reliable

[ZacharyB]

Saving prompts as templates and naming them helps speed repeats

[AnthonyRH]

If you want a concrete example I keep a prompt that asks the model to output a concise summary with key takeaways and a one sentence action item It works well for quick briefs