Common mistakes when writing prompts

The ten most common prompt-writing mistakes and how to avoid them: from missing context to blindly trusting the answer.

These are the mistakes we see most often and how to fix them.

1. Being too vague

“Help me with my website” forces the AI to guess. Specify which part, which problem and what result you expect.

2. Giving no context

The AI does not know your business, audience or situation. Whatever you do not tell it, it assumes.

3. Asking for too much at once

A single prompt with ten tasks produces ten mediocre results. Split it into steps and review each one.

4. Not specifying the format

If you need a table to paste into a spreadsheet, ask for it. Otherwise you will get paragraphs.

5. Contradictory instructions

“Very detailed” and “very short” at the same time. Reread the prompt before sending it.

6. Accepting the first answer

The first result is a draft. Ask for specific improvements.

7. Not verifying data

Models can invent figures, quotes, links or references with complete confidence. Always check what matters against reliable sources.

8. Sharing sensitive information

Do not paste passwords, customers' personal data, medical records or confidential company information into tools that are not authorized for it.

9. Using AI for decisions that need a professional

AI can help you understand a legal, medical or financial topic and prepare questions, but it does not replace a licensed professional.

10. Not saving the prompts that work

When a prompt works, save it as a template with variables. It saves time and gives consistent results. You can also save the library prompts you use most to your Favorites.

Learn prompting

  • What is a prompt and how does it work

    A prompt is the instruction you give an artificial intelligence. Learn its parts, why the result depends so much on how you write it, and how to get started.

  • How to create a prompt from scratch

    A 6-step method to go from a vague idea to a clear, reusable prompt, with a complete end-to-end example.

  • How to write effective prompts

    Ten practical principles to write prompts that work on the first try more often: clarity, specificity, examples, format and review.

  • How to write prompts for ChatGPT

    Practical tips to get more out of ChatGPT: message structure, custom instructions, long conversations, files and reviewing answers.

  • How to write prompts for Claude

    How to write prompts for Anthropic's Claude: direct instructions, rich context, long documents, tags for structure and requests for reasoning.

  • How to write prompts to generate images

    The structure that works best in Midjourney, DALL-E, Stable Diffusion, Flux or Firefly: subject, style, light, composition and parameters, with examples.

  • How to write prompts to generate videos

    How to describe a shot for Runway, Kling, Sora or Veo: one subject, one action, camera movement, light and mood. And how to plan multi-shot videos.

  • How to improve a bad prompt

    A diagnostic process to find out why a prompt is not working and how to fix it, with a before-and-after example and a prompt to let the AI help you.

  • What are variables in a prompt

    Variables turn a prompt into a reusable template. Learn how to define them, name them and use them on HomeCasa Prompts.

  • What is context in a prompt and how much to give

    Context is the information the AI cannot guess. Learn what to include, what to avoid and how to organize documents and data inside a prompt.

  • How to define roles in a prompt

    What telling the AI “act as…” really does, how to choose a good role, combine it with concrete criteria and avoid roles that do not help.

  • How to structure prompt instructions

    Numbered steps, delimiters, sections and logical order: how to organize a long prompt so the AI does not skip anything.

  • Good and bad prompt examples

    Eight comparisons of real prompts for text, code, image and video, explaining what fails in the bad version and what the good one improves.

  • Advanced prompting techniques

    Few-shot examples, step-by-step reasoning, prompt chaining, self-critique, structured outputs and evaluation: techniques for more reliable results.