What is Prompt Engineering?

With artificial intelligence (AI) and natural language processing (NLP) becoming increasingly more prevalent in business for content creation, language translation, customer support, and many other areas, it’s important for users to understand how to utilize these powerful tools effectively. 

With the stratospheric interest in leveraging large language model’s (LLM) breakthrough capability, prompt engineering is a highly needed skill. Empowering the art of prompt engineering allows users to harness AI’s potential like never before.

In this blog, we’ll explain prompt engineering, why it matters, and what problems it can be used for when using LLMs. Whether you’re an AI enthusiast, a developer, or simply curious about how AI systems understand and respond to your queries, this exploration will demystify the concept and reveal its pivotal role in shaping some of the AI-driven experiences we encounter daily.

What is a Large Language Model and What are Prompts?

A large language model is a type of machine learning model that is designed to understand and generate human language. These models are trained on vast amounts of text data to learn the patterns and structures of language, modeling them with deep neural networks. These models are being rapidly adopted for various applications across industries and have significantly advanced the field of NLP.

Simply put, prompts are the user input into these models. They are typically a few sentences (stretching to whole books in some cases) that dictate the type of response expected out of the LLM.

What is Prompt Engineering and Why Does it Matter?

Prompt engineering is a critical and nuanced aspect of working with LLMs like OpenAI’s ChatGPT, Microsoft’s Bing Chat, or Google’s Bard, as it involves carefully crafting the input instructions or queries to elicit desired responses or outputs from the model. 

This practice is essential because LLMs, while highly capable, can produce a wide range of responses based on the input they receive. By designing effective prompts, users and engineers can harness the power of these models to meet specific application requirements.

What are the Top Problems Prompt Engineering Can Solve?

Information Retrieval/Question Answering

One of the primary benefits of prompt engineering is that it enables users to clarify their search intent. Rather than relying on generic queries, users can design prompts that explicitly state what they are looking for. This specificity helps the LLM understand the user’s intent more accurately.

In cases where users have complex or multifaceted queries, prompt engineering can break down the query into sub-questions or include qualifiers to guide the model’s response. This approach is especially valuable when searching for information on diverse or intricate topics.

Let’s go step-by-step through several prompt engineering use cases and show how specific language can significantly improve results.

Example of Information Retrieval/Question Answering Without Prompt Engineering

The LLM might return a range of results, including articles, books, and documents that mention the causes of World War II. However, the results may vary in relevance, and the model might not fully understand the context of your query. You may need to sift through a large volume of documents to find the specific information you are looking for.

With Prompt Engineering

Since explicit instructions are provided to the LLM, specifying the context (World War II causes), the type of information you want (concise summary), and the aspects you’re interested in (political, economic, and social factors). This prompt guides the model to understand your intent better.

As a result, the LLM is more likely to generate a focused and relevant response that directly addresses your query. It can provide a brief summary of the key causes of World War II, making it easier and more efficient for you to retrieve the information you need.

Content Generation

Prompt engineering refines content generation, allowing users to direct LLM outputs more accurately. Through precise prompts, users define the content’s topic, tone, style, and target audience, ensuring relevance and intent alignment. This technique also enables the inclusion of essential details or constraints, making the content engaging and tailored.

Additionally, prompt engineering considers ethical aspects, such as plagiarism prevention and style adherence, guaranteeing high-quality content. It simplifies complex tasks into clear subtasks, enhancing comprehensive content creation. Users can iteratively adjust prompts, refining content based on evolving needs.

For various stakeholders, including businesses and content creators, prompt engineering streamlines production, ensuring consistent messaging. It also broadens content versatility, accommodating different languages, audiences, and creative directions. This customization lets users leverage LLMs for efficient, tailored content across numerous sectors.

Example of Content Creation without Prompt Engineering

Without prompt engineering, the NLP model will generate a description based solely on the minimal input it receives. The resulting content might be generic and lack important details or unique selling points.

With Prompt Engineering

With prompt engineering, you’ve specified the product (XYZ smartphone model) and outlined the specific features you want to emphasize. This detailed prompt guides the LLM to generate content that is tailored to the product’s unique selling points.

Language Translation

Prompt engineering enhances language translation by providing clear instructions and context to LLMs. It allows users to specify source and target languages, tone, and formality, ensuring accurate translations. By incorporating surrounding sentences or industry-specific jargon, the model can produce translations that are contextually fitting and industry-relevant. 

This is especially beneficial for idiomatic expressions and phrases without direct equivalents.

Additionally, prompt engineering enables customization. Users can define the style, cultural nuances, or regional variations, ensuring translations resonate with the intended audience. An iterative approach to refining prompts ensures continuous improvement, aligning with changing user needs. 

In essence, prompt engineering improves accuracy, relevance, and tailoring in translations, promoting effective cross-cultural communication for global audiences.

Example of Language Translation without Prompt Engineering

Without prompt engineering, the LLM will perform a straightforward translation but might not fully capture the context or nuances of the sentence. The translation might read: “Il parle trois langues couramment”

While this translation is valid, if you want to ensure that the translation reflects a specific context or emphasis, you can use prompt engineering to provide additional guidance:

With Prompt Engineering

This prompt gives the model context about the desired emphasis and may result in a translation that maintains the emphasis on fluency, possibly leading to a translation like: “Il parle couramment trois langues.”

Code Generation

LLMs are capable of generating code snippets or automating coding tasks, but getting the correct code needed out of a model can be challenging without using the correct prompt. 

Prompt engineering allows users to specify the task in detail, including the programming language, input data, desired functionality, and constraints. This clarity helps the LLM understand the coding task accurately.

Users can use prompts to control the coding style and conventions they prefer, such as following a coding standard, naming conventions, or formatting rules. This ensures that the generated code aligns with their preferred coding practices.

Lastly, for intricate coding tasks, prompt engineering can break down the problem into smaller, more manageable sub-tasks. Users can design prompts that guide the model to solve one part of the problem at a time, simplifying the overall code-generation process.

Example of Code Generation without Prompt Engineering

Without prompt engineering, the LLM will generate a code snippet but might not consider specific details, error handling, or user preferences. The generated code could look something like this:

While this code calculates the factorial correctly, it lacks comments, error handling for negative numbers, and customization options.

With Prompt Engineering

Instead of a generic prompt, you provide a well-structured and detailed prompt like:

With prompt engineering, you’ve specified important details, including comments for clarity, error handling for zero, and the option to customize the function name.

The resulting code might look like this:

Prompt engineering ensures that the generated Python code is not only correct but also well-documented with comments, handles errors for negative numbers, and allows for customization of the function name. It demonstrates how prompt engineering can be used to tailor code generation tasks to specific requirements and preferences, resulting in more robust and user-friendly code snippets.


Prompt engineering, as we’ve discovered, empowers users, developers, and enthusiasts alike to sculpt AI-powered interactions into precision instruments. By crafting specific instructions and context, we’ve seen how it breathes life into LLMs, enabling them to provide tailored responses, generate content with finesse, and unlock the treasure troves of knowledge they contain.

Interested in deepening your understanding of AI and prompt engineering?

Check out our trending guide that shows you the steps to kickstart the integration of AI into your business.


The difficulty of mastering prompt engineering depends on your background, experience, and the specific goals you have in mind. Like any skill, prompt engineering requires practice and a willingness to learn from your experiences. It may take time to become proficient, especially if you are new to the field. Prompt engineering requires a high attention to detail, as well as strong communication skills as detailed prompts can be quite complex and involve hundreds, even thousands, of carefully chosen words.

Although it is a fairly recently created practice, there are many resources available to learn to be more effective at prompt engineering including online courses, books, online communities and YouTube videos. However, prompt engineering is learned through practice first and foremost.

Data Coach is our premium analytics training program with one-on-one coaching from renowned experts.

Accelerate and automate your data projects with the phData Toolkit