Common Prompting Mistakes (and How to Avoid Them)

The output of a Large Language Model is only as good as the instruction you give it. Yet, in practice, we often see developers and users falling into the same traps. Here are 8 common prompt engineering mistakes and how to solve them.

1. Formulating a vague or too general goal

If you are not specific, the model will fill in the gaps itself. This often leads to generic, superficial output that doesn't quite align with your use case.

2. Leaving out essential context

LLMs have no implicit knowledge of your specific situation. Without background information, it is impossible for the AI to give accurate or safe advice.

3. Cramming too many tasks into one prompt

When you ask a model to simultaneously analyze data, devise a strategy, write code, and format text, the quality of each individual part drops drastically. Break down large tasks.

4. Not specifying an output format

Do you need data for an application? If you don't specify exactly which format (such as JSON, CSV, or YAML) you want to receive it in, you will likely get a mix of plain text and code blocks back.

5. Negative instead of positive instructions

Models focus heavily on the words in your prompt. If you say "do not use complicated words", the AI focuses its attention precisely on 'complicated words'. Instead, tell the model what it should do.

6. Ignoring the power of "Few-Shot" examples

The concept of 'zero-shot' (asking something without examples) works for simple tasks, but for complex formatting or data registration, one or two concrete examples (few-shot) are worth their weight in gold.

7. Not configuring the role (Persona)

By default, an LLM responds as a helpful, general assistant. By assigning a specific role or persona, you enforce a certain level of expertise and language style.

8. Too rigid and over-specified constraints

Sometimes developers try to force the model into too tight a straitjacket with arbitrary rules, which comes at the expense of the fluency or logic of the response.