English | EN 
Basic prompt example:
“Translate the sentence ‘Hello’ into Italian.”
Prompt engineering focuses on designing, testing, and optimizing instructions (prompts) to effectively leverage Large Language Models (LLMs). This process helps users better understand the capabilities and limitations of LLMs and improve their performance across a wide range of tasks, including question answering, content generation, and information extraction.
One common misconception is that prompt engineering only consists of writing better prompts. In reality, it also involves evaluating outputs, experimenting with different strategies, and combining LLMs with external tools.
A prompt can contain the following elements, although not all of them are required. Their use depends mostly on the task.
Instruction is the most crucial element; It refers to a task or a specific instruction you want the model to perform. Note that instructions should be clear and unambiguous.
Context is also extremely important, as additional information or background knowledge helps the model better understand the intent of the user and generate a more relevant response.
Input Data: The question, text, or other input for which a response is expected.
Output Indicator: A description of the desired output format, structure, or style.
When designing a prompt, it is important to consider the constraints that may influence how an AI model interprets instructions and generates responses. First, length refers to the amount of information provided in the prompt. It can significantly influence the quality of the output. Finding the right balance is essential: too little information may lead to vague responses, while too much can overwhelm the model. Next, complexity should be adapted to the target audience and phrased in a way that minimizes ambiguity. Tone is another important factor, as it influences the style and formality of the response generated by the AI. Format is also crucial. Specifying the expected format increases the likelihood of obtaining the desired result.
For example:
“Use short paragraphs.”
All of these constraints help produce responses that are more precise, coherent, and better matched to the user’s needs.
Improving prompting helps achieve better results across a variety of tasks. To achieve this, a wide range of prompting techniques has been developed, each aiming to guide the model’s behaviour and improve the quality and relevance of its outputs.
Let’s look at some of the most popular prompting techniques:
- Zero shot prompting: All you need to do is provide instructions to the model without giving examples. It is particularly useful for classification and information extraction tasks.
- Few-shot prompting: Unlike zero-shot prompting, users provide a few examples to guide the model toward the desired output while also helping it better understand the context.
- We can take it a step further with the Chain-of-Thought Prompting. This technique encourages the model to generate intermediate reasoning steps before producing the final answer. It is quite effective for complex reasoning or mathematical tasks. But prompting techniques do not stop there.
For example, if we ask an LLM to summarize a customer review, a zero-shot prompt may generate a generic answer. By providing two or three examples through few-shot prompting, the model generally produces more consistent and accurate outputs.
Prompt engineering is often the first step toward unlocking the full potential of Large Language Models. While techniques such as zero-shot, few-shot, and Chain-of-Thought prompting provide a solid foundation, they represent only a small part of a rapidly evolving field. In future articles, we will explore more advanced approaches, including prompt optimization, retrieval-augmented generation (RAG), tool use, and AI agent frameworks, to better understand how modern AI systems can be guided to solve increasingly complex tasks.
References:
A Deep Dive into Prompt Engineering and Generative AI, Chinaza Chukwunweike 2025
Advanced Prompting Techniques and Embeddings in AI, Yashasviben, 2026