Not a thousand templates you’ll never remember. Six short lessons on why AI gives bad answers and how to fix it, each one paired with a tool you can try right now. By the end, you’ll prompt better on any topic, forever.
A model can’t read your mind. Every detail you leave out, who it’s for, what it’s for, how long, it guesses, and guesses differently each time. Most “bad AI answers” are really just under-specified prompts. Start by seeing what your prompt leaves to chance.
Try it: Prompt SimulatorBefore anything else, say what success looks like. “Write a homepage” has no goal; “write a homepage that gets visitors to start a free trial” does. A goal turns vague output into output that’s trying to do something.
Try it: Prompt BuilderThe same request needs opposite answers for a beginner and an expert. Tell the model who it’s writing for and the depth, vocabulary, and tone fall into place on their own.
Try it: Prompt BuilderThe model only knows what’s in the prompt. Paste the article, the code, the data, the brand voice, whatever it needs to work from. Without context it fills the gap with generic filler. X-ray your prompt to see if the context is actually there.
Try it: Prompt X-RayConstraints are where quality lives: length, what to avoid, what to include, the rules. “In 3 bullets, no jargon” beats “keep it short.” Watch out for conflicting ones (“detailed but brief”), the linter catches those.
Try it: Prompt LinterSay exactly what you want back: prose, bullets, a table, JSON, a specific shape. Unspecified format is the number-one cause of “it almost worked.” The before/after gallery shows the difference a format line makes.
Try it: Good vs Bad PromptsGoal, audience, context, constraints, format. Answer those and you’ve written a strong prompt, no formula required. When you’re iterating, compare versions with Prompt Diff, and when you’re ready to go deeper, the full learning paths and knowledge map cover how the models work underneath.