Mistake #1: Too general a query without context
The most common mistake is a request like “write a text about coffee.” The model does not know who the text is for, why, what length and in what tone, so it produces average water that suits everyone and no one. The neural network does not read thoughts: it completes what is missing according to average probability. Give her some introductory information - who you are, who the text is for, what the purpose is, where it will be published. Compare: “write about coffee” and “write a paragraph for a product card in an online store, the audience is young parents, the tone is warm, the task is to explain why freshly roasted beans taste better.” Conclusion: context is not politeness, but fuel for an accurate answer.
Mistake #2: You didn't specify a role
The model responds differently depending on “who” you asked it to be. Without a role, she keeps herself neutral and streamlined, like a reference book. Add a role and the depth, vocabulary and focus change. “Explain taxes” will give a dry summary, but “you are an accountant, explain taxes for the self-employed in simple words, like a friend over tea” - a clear, lively explanation with the necessary details. The role sets the perspective and level of expertise. The rule is simple: before the task, indicate in the role of which specialist and for which reader the model is working. Conclusion: one line with a role often changes the quality of the answer more than changing the neural network itself.
Error #3: response format not specified
You received an answer on the topic, but it is impossible to use it: a solid sheet of text when you needed a list, or a paragraph when you needed a table. The model guesses the format itself, and it doesn’t always guess. Say directly: “form it in a table with columns X and Y,” “give five points in one sentence,” “answer in the form of a ready-made letter without explanation.” At the same time, set the volume: “up to 100 words”, “three headline options”. The format is what turns the answer from “interesting to read” to “can be immediately put into work.” Conclusion: describe not only what to say, but also how to package it.
Mistake #4: everything in one prompt instead of steps
Newbies try to fit everything into one giant request: “analyze the market, come up with a product, write a plan, create a post and calculate the budget.” The model grabs onto everything at once and does each point superficially. It works differently - step by step, as with a live assistant. First: “let’s define the target audience, ask me clarifying questions.” Then, based on the answer, the next step. Dialogue allows the model to rely on intermediate results and hold the thread. Divide a complex problem into 3–5 moves and check each one. Conclusion: one precise step at a time almost always hits one huge prompt for everything at once.
Mistakes No. 5 and No. 6: no example and no restrictions
Two errors that are easy to fix. First, you didn't show an example. If you need a specific style or structure, give an example: “this is how our texts sound: ... - do the same.” One or two examples (this is called a few-shot) guide the model more accurately than any adjectives. The second is that no restrictions are set. The default model is verbose and cautious, so say what not to do: “no bureaucratic stuff,” “don’t use exclamation points,” “keep it to 80 words,” “don’t make up facts that aren’t in the introduction.” Boundaries discipline the response. Conclusion: the example shows where to go, and the restrictions show where not to go.
Mistake #7: Blind trust without checking the facts
And the main mistake is to take the answer on faith. The language model sounds confident even when it's wrong: it can make up a fact, a date, a quote, or a link because it generates plausible text rather than checking against the truth. This is called hallucinations. Check everything that ends up in an important document, a letter to a client or a publication against primary sources - especially numbers, names, legal and medical details. Use the neural network as a quick and intelligent author of drafts, and not as a last resort. Conclusion: trust the model for speed and form, but leave the facts behind - this way you get the benefits of AI without its risks.