Prompt engineering refers to writing precise and well-structured prompts to better interact with LLMs models, such as ChatGPT and Google’s Gemini. This helps us get better—more precise and accurate—answers from these models and their likes.
In this article, we will discuss prompt engineering, types of prompts, and its importance to help you improve your interactions with AI.
Prompt engineering is just a fancy word to mean “effective prompt writing.” As it clarifies, it is the practice of creating effective prompts to guide AI models (like ChatGPT) in generating a more high-quality response compared to generic responses.
AI models generate text based on probabilities derived from huge datasets. So, the way a given prompt is phrased affects the model’s output significantly. That’s why a well-structured and -phrased prompt can get an AI model to be more accurate, creative, and specific in its response.
On the other hand, a poorly written, unstructured prompt can do the opposite: lead to vague and off-topic responses.
Although prompt engineering can be used by anyone, in any field, it is most useful in various fields where AI-generated responses need to be precise, context-aware, and relevant, including:
More, but these areas specifically can use AI prompt engineering to enhance the effectiveness of its outputs, making human to AI interactions more reliable and efficient.
Prompt engineering involves some techniques like giving the AI tool clear instructions about your prompt, asking the tool to use a specific format, giving examples, and refining the prompt iteratively based on AI responses.
For example, asking “Explain computer programming to a 12 year old, covering essential skills and demand in 2025” is better than asking “Explain programming and if it’s worth it or not.” The former is more detailed compared to the later—it is engineered properly.
There are many prompt engineering techniques that can be used to guide the AI tool to respond as needed. Some of the essential techniques include:
Let’s discuss these techniques below:
Zero shot prompting is probably the most commonly used prompt engineering method. It asks the AI model to perform a task without providing any examples, instructions, or context of the problem. The LLM relies on its previously-trained knowledge to generate an accurate response. For example, you can ask ChatGPT, “Write a summary of this article.” The tool will attempt to summarize the article based on its general understanding of how summaries are written.
Another example is asking the AI to: “Rewrite and punctuate this text.” The tool will attempt to follow the prompt based on its training.
The technique is called “zero-shot” because the model is provided with zero examples before generating its response.
It is particularly handy for straight-forward tasks that don’t need explaining, like answering a factual question. However, the technique has its limitations: It is not suitable for complex tasks, and tasks that require context awareness. Plus, the succeeding responses from the model are likely to be inconsistent since it’s not been instructed.
Few-shot prompting, as the name indicates, is a type of prompting technique in which the user provides the AI model with a few examples of what the tool needs to do prior to generating response. For example, the following prompt is an example of few-shot prompting as it lists out some examples:
“Suggest cute names for my kitten. For example, tubby, chubby, mimi, and taro.”
Unlike zero-shot, few-shot prompting helps guide the AI by showing patterns, formats, or expected responses. The AI model guides and refines its response based on the given examples to meet the user’s requirements. Thus, few-shot prompting not only relies on the model’s pre-trained knowledge but also on users’ indicated patterns.
Few-shot prompting is particularly helpful for tasks that need a more nuanced and refined answer.
Chain-of-thought prompting is another useful prompt engineering technique. Also called CoT prompting, this technique asks AI models to break down their reasoning process in steps before arriving at a final answer.
So, instead of giving a direct response, the AI tool is prompted to explain its thought process logically, explaining how it came to the conclusion it came to. The CoT technique improves the model’s performance in tasks that take complex reasoning, such as math problems, logical reasoning, and multi-step decision making.
For example, take a look at how ChatGPT responds to the following two commands, the first of which is a zero-shot prompt and the second a chain-of-thought prompt:
As you can see, the ChatGPT provided a detailed and logically explained answer to the CoT prompt compared to the zero-shot prompt. This technique can be used to prompt models to reason well before arriving at a conclusion to get more well-reasoned and well-explained answers.
CoT prompting is particularly useful for tasks involving math, logic puzzles, and critical thinking.
As with other techniques, CoT has some limitations, including potentially slower responses and increased token usage.
AI prompts need to be well-written to be effective. This means you have to make sure your prompt is clear and precise. Following are some tips to help you refine your prompts:
Prompt engineering refers to crafting effective prompts that help AI tools generate better responses. There many different prompt engineering techniques, of which Chain-of-Thought (CoT), which is useful for complex tasks; Zero-shot, which is useful for straight-forward tasks, and Few-shot, which is useful for tasks that need some guidance, are some of the common ones to name.
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