If you didn’t already know — or maybe you’ve guessed it by now — an AI model works best when it receives clear, structured, and well‑defined instructions. And this isn’t visible only in the prompts we write, we can also infer it directly from the answers we get from an AI model. Most of the time, if we don’t explicitly ask for a certain format for the answer, the AI model will often use ordered structures such as: tables, bullet points (sometimes it even goes overboard with bullet points), numbered lists, subheadings, etc. This happens because the AI model understands and generates coherent, orderly data structures more easily.
Yes, we can write a prompt as a long block of text, with mixed-up sentences and little punctuation, and the model will still try to respond and will even give an answer. But it may confuse or fail to understand certain requirements, and then the answer won’t be what we expect.
But if we want to maximize and optimize the result, there is a much more efficient method: using a logical structure that models like ChatGPT, Claude, Gemini, or Mistral can understand and interpret more accurately.
This structure is not something new, there are others as well, but in my experience it delivers some of the most optimized results. The structure can be built very simply in Markdown format, using headings and sections that start with #.
Why a clear structure works better for AI
Artificial intelligence models are trained on huge amounts of text, code, and structured data. These data are fed into the training process, in the initial phase, as raw — pretty much everything that can be found on the internet, whether it’s useful or not, regardless of the quality of the information. In the next stages of model training, various cleaning and optimization operations are performed to configure the model to give the best possible answers. However, these answers are a kind of probability that results from the huge amount of information ingested at the beginning. That’s why it’s useful to ask for what we want in a structured way, so that we increase the chances of getting an answer as close as possible to what we expect.
When it receives information that is:
- well organized,
- split into sections,
- with clear boundaries between goal, requirements, and context,
the model can:
- interpret the request more accurately,
- reduce hallucinations,
- deliver more coherent results,
- maintain style consistency,
- and avoid misinterpretations.
The optimal prompt structure compatible with any AI
This is a universal, modern, and already established variant in prompt engineering. As I mentioned above, there are others, but this one has delivered the best results in my experience.
It applies to GPT-4.1 / GPT-5.1, Claude 3.5/3.7, Gemini, as well as Mistral or Grok.
—
# Task goal
Clearly describe what the AI model needs to do.
# Requirements and criteria
List the rules, constraints, mandatory details, and desired style.
# Context
Provide additional information about the situation, audience, domain, or purpose.
# Available resources or tools
Include data, files, links, examples, or tools the model can use.
# Warnings / Limitations
Specify what the model must NOT do: no hallucinations, no unjustified assumptions, no fabrications.
# Examples
Provide input/output samples that guide the AI.
# Output format
Define how the answer should look: Markdown, table, list, plain text, etc.
—
This structure, although simple, is one of the most reliable ways to obtain consistently high-quality results.
Practical example: simple prompt vs optimized prompt
Weak prompt
Give me some good laptop recommendations for gaming.
The result?
– general, vague, unstructured.
Optimized prompt
# Task goal
Recommend 5 good gaming laptops under €1500.
# Requirements and criteria
– GPU performance in 1080p
– Good cooling
– Proven reliability
– Models available in Europe
# Context
Article for a tech blog where readers prefer straightforward explanations, without over-the-top marketing.
# Output format
Table with: model, minimum estimated price, GPU, processor, average FPS score + a short conclusion.
The difference in quality is dramatic, even though the AI model is the same.
Tools that can help you generate better prompts
If you want to go one step further, you don’t always have to manually build the perfect prompt. There are already tools and platforms that help you build well-defined prompts.
Claude – Prompt Generation
This one is geared towards the Claude ecosystem and is not free. Because it runs on Claude’s API infrastructure, you pay for it as you would for API usage.
It’s especially useful if you’re already working with the Claude API or building agents/clients that depend on their ecosystem.
ChatGPT / GPT-4 – free “Prompt architect”
In ChatGPT you can turn the model into a prompt generator without paying anything extra, just by using the instruction:
“From now on, act as a Prompt Architect. For every request I give you, propose 2–3 variants of an optimized prompt, in a structured format (goal, requirements, context, output format).”
Basically, you use the AI to generate prompts with the same structure presented in this article.
Google Gemini – prompt refining and templates
You can give it a “one-block” prompt and explicitly ask it:
“Rephrase this prompt into a clear structure with: task goal, requirements, context, examples, and output format.”
Poe, PromptPerfect and other prompt generators
Poe has specialized bots for prompts (Prompt Creator, Prompt Optimizer), which can generate complex structures starting from a simple request.
PromptPerfect also offers a free version, where you enter a short prompt and receive a “long”, optimized version for multiple models.
These tools don’t perform magic; they apply some best-practice patterns, and often the prompts they generate follow exactly the structure above: goal, requirements, context, examples, format.
Conclusion
A prompt can be just a question thrown into the chat — but if we have a more complex request and want an answer as close as possible to our expectations, it’s recommended to formulate it as a set of structural instructions.
The better organized it is, the more the AI will deliver results that are:
- clearer,
- more accurate,
- easier to use directly in articles, code, or documentation.
Modern AI models work exceptionally well with prompts built on the structure:
goal → requirements → context → resources → warnings → examples → output format.
And if you want to go further, you can also use the tools mentioned above to automate part of this process and see in practice what a “well-crafted” prompt looks like.
