In artificial intelligence, a prompt is simply an input or set of instructions given to an AI solution—typically a large language model (LLM)—to guide its output.

An example of a prompt might be: "Draft a company memo reminding employees about the CRM training course at the end of the month." Provided it has all the necessary context, an enterprise AI tool can then generate a response based on these instructions.

What is prompt engineering in AI?

Artificial intelligence prompt engineering is the process of developing and iterating instructions to optimize the performance of a large language model, particularly in complex or nuanced tasks. This makes it vital for the effective implementation of generative AI.

AI prompts come after the pre-training phase, which involves exposing large language models to massive datasets to build foundational language understanding. Once these models are trained, a second phase of fine-tuning and prompt engineering begins. In this phase, inputs are refined to guide outputs and performance for specific applications.

How does prompt engineering work?

LLMs use machine learning to analyze massive datasets and generate responses (outputs) based on patterns they've learned from their training data. A prompt acts as an instruction to the AI system on what action to take.

How the prompt is written can significantly impact the quality of the output. Simple, well-written, and clearly structured prompts typically lead to more relevant results. Vague or unclear AI prompts risk producing confusing, irrelevant, or inaccurate outputs.

Prompt engineering also involves testing and refining prompts to achieve the best outcome. For example, you might first instruct an AI solution to “explain our new CRM software to staff.” Refinement, however, may lead to the prompt “Write a memo to our sales team highlighting the key features of our new CRM software,” which gives more context and direction.

Adding examples to prompts can guide the AI tool in producing better responses. This enables prompt engineers to tailor the solution to perform certain industry- or domain-specific tasks, address specific business challenges, or communicate in a specific style or tone.

Carefully and thoughtfully designed prompts make LLMs more effective and reliable. Prompt engineering is an important skill for any business looking to maximize the use of generative AI tools.

Prompt engineering use cases

Prompt engineering helps organizations optimize AI for practical, industry-specific applications.

Financial services

Banks and financial institutions can use prompt engineering to streamline reporting and analysis. For example, financial analysts might use the prompt "generate a report summarizing quarterly market trends and investment performance."

This can help to simplify complex tasks and deliver insights faster, particularly when paired with technologies like retrieval-augmented generation (RAG).

Healthcare and life sciences

In frontline medical settings, prompt engineering can help staff draft clinical notes and referral letters. For instance, a clinician might use a prompt to summarize a patient’s medical history, current medications, and symptoms they present during consultation. A generative AI tool can then create the draft, which the clinician reviews and edits for accuracy.

Custom AI virtual agents are also being developed using specialized prompts to assist with specific healthcare challenges, such as obesity management and chronic diseases. Prompts guide these virtual AI assistants to provide targeted recommendations based on patient information like symptoms, lifestyle habits, and diagnostic data. This enables the AI tool to provide targeted advice or refer patients to clinicians for review.

Public sector

Governments worldwide are also starting to explore generative AI tools to support civil servants in their day-to-day duties. The operational efficiencies that can be gained through trusting repetitive manual and administrative tasks to enterprise AI can help reduce costs, streamline data gathering, and improve public communications.

For example, a tax department could find it useful to implement an AI-powered virtual assistant that can assist citizens in filing taxes. Effective prompt engineering can guide the machine to help taxpayers through the various steps involved in tax filing, and answer questions related to eligibility, deadlines, and deduction. Good prompt engineering can make the difference between a generic assistant and one that guides citizens through the maze of tax procedures.

Energy and utilities

In the energy sector, prompt engineering is being used to automate building energy modeling, a process that allows simulations of how a building will consume energy.

Prompt engineering can also help architects and building engineers guide LLMs in generating accurate energy models without needing specialized software expertise. This potentially simplifies the modeling process, saves time, and makes energy modeling more accessible to non-experts.

Examples of prompt engineering techniques

Basic prompt engineering is highly accessible. Employees across various functions and departments can use AI prompts to support productivity in tasks like content generation and admin work.

Mastering more advanced prompt engineering—such as designing AI prompts for complex, high-stakes, or domain-specific applications—typically requires a deeper understanding of AI principles like natural language processing (NLP) and machine learning.

Prompt engineers use techniques to improve the outputs of AI models, such as in the examples of prompt engineer techniques below..

Generated knowledge prompting

Generated knowledge prompting involves asking an AI to produce relevant facts, context, or background information before tackling a specific task or query.

For example, a user may ask the AI system to draft an essay on solar energy. The model may be prompted to first: “List the key benefits of solar energy.” This initial response is then used to complete the main task: “Now put these benefits into a slide for a presentation on sustainable energy.”

Generated knowledge prompting helps AI tools provide more accurate and relevant results by providing them with extra context.

Self-refine prompting

Self-refine prompting instructs AI tools to evaluate and improve their own outputs through iterative refinement.

For example, a marketing team might prompt a model to create a product description for a new gadget, then analyze it for tone and relevance. The AI could refine its initial draft, adjusting the language to better highlight the product’s features and appeal to the target audience. This process continues until the output meets the standards or criteria required by the marketing team.

Chain-of-thought prompting

Chain-of-thought prompting guides LLMs to tackle complex tasks by encouraging them to break down the problem into intermediate reasoning steps.

Instead of providing a direct answer, the model outlines a logical sequence of smaller steps that lead to its conclusion. Chain-of-thought prompting helps improve the accuracy and transparency of AI tools because it allows users to follow and verify the model’s reasoning process.

Least-to-most prompting

This technique works by breaking down complex tasks into simpler "subproblems" that are solved incrementally.

After receiving a complex prompt, the AI model breaks it down into manageable, bite-sized tasks. Once identified, it addresses each task sequentially, iteratively applying solutions from earlier steps to help inform the subsequent tasks.

Least-to-most prompting is particularly effective for tasks that require clear reasoning and for solutions to be built progressively. The technique enables AI tools to address challenges that are more complex than the examples in its training data.

Directional stimulus prompting

Directional stimulus prompting provides the model with tailored hints or cues, known as “directional stimuli,” to guide it toward generating desired outputs. These stimuli are customized for each input and are specific prompts that refine the model’s responses.

For example, directional stimulus prompting could guide an AI system when summarizing patient records in a healthcare setting. In this context, a stimulus might specify keywords like “medications,” “symptoms,” and “diagnosis,” prompting the AI tool to prioritize these details in its summary. This ensures that the generated output is concise and focuses on key patient information that can improve clinical decision-making.

Benefits of prompt engineering

AI prompt engineering helps make an AI tool more accessible to end users. It delivers a range of benefits for enterprise generative AI roll-outs, such as these:

By refining the language and context of the prompts, it enables AI tools to deliver more accurate and actionable results tailored to specific needs. For example, a good prompt can instruct an AI system to focus on certain aspects of the input data (e.g., key metrics like customer satisfaction scores), ignoring irrelevant ones.
It can reduce time spent on repetitive or data-heavy tasks like report generation or workflow optimization thanks to automation. For example, a well-crafted prompt that tells the AI to generate reports by focusing on certain inputs such as trends, revenue, or expenses can streamline the overall reporting process.
With effective prompt engineering, enterprises can simplify complex tasks and achieve high-quality outputs, often without requiring deep technical expertise. This is because, provided with effective prompts, even non-technical staff can use AI tools to generate outputs useful to their work.
Custom prompts enable AI solutions to be tailored to specific industry goals, such as improving patient outcomes in healthcare or optimizing energy use in utilities. In healthcare, for instance, a provider can use specific prompts to prioritize the extraction of patient data related to symptoms or medications.
Crafting and tuning effective prompts can reduce mistakes and increase reliability when working on complex or high-stakes tasks. Good prompt engineering helps businesses minimize ambiguity in results and reduce the likelihood of costly mistakes.
Strategic prompt engineering can help steer AI systems to explore different angles of the same problem and observe different potential scenarios based on data.

Challenges of prompt engineering

The theory behind prompt engineering is relatively simple. In practice, however, prompting effectively isn’t always straightforward. Here are some possible challenges to be aware of:

Writing effective prompts often requires multiple iterations to get the desired result. Iterative testing and refinement are key to achieving high-quality outputs.
Prompts designed for singular tasks are likely to perform poorly in other scenarios. Creating industry-specific templates or reusable prompt libraries provides proven frameworks that save time and improve reliability. This also makes it easier to achieve consistent results.
AI tools may produce incomplete or irrelevant responses without clear guidance. You should always strive to provide detailed context or examples in your prompt to help ensure that outputs are useful and accurate.
However, stuffing a prompt with unnecessary details can confuse AI models and reduce quality. Write clear, concise prompts containing only essential information, and you’ll typically get clearer and more consistent results.
Poorly-constructed prompts can lead to biased or inappropriate outputs. Test prompts in various use cases and scenarios to help identify and minimize these risks.
Even with well-designed prompts, LLMs may struggle with tasks that require expertise beyond their training. Human oversight, combined with AI solutions and RAG capabilities, can greatly improve relevance and accuracy.

How to effectively use prompt engineering: best practices

To get the best out of your prompts, use a checklist to guide you through the essentials. Here are some best practices to keep you on the right track.

Define the goal clearly. Begin with a clear understanding of the task or problem you want the AI tool to address. Specify what success looks like, whether it’s summarizing a report, generating ideas, or troubleshooting a technical issue.
Use role-based prompts. Assign a role to the AI tool in the prompt to guide its tone, style, and focus. For example: “You are a project manager. Write a report summarizing the key milestones of this quarter.” This helps ensure the output aligns with the intended perspective and priorities.
Iterate and refine. Expect to test and revise prompts multiple times before you get the desired results. Start with a basic prompt and adjust it based on the model’s response. Include specific examples, clarify vague instructions, and remove any unnecessary details during each iteration.
Be specific and concise. Avoid overloading the prompt with too much information. Focus on essential details that are critical to achieving the desired output. A concise prompt reduces confusion and increases accuracy.
Provide context when needed. Include relevant background information or examples to help the AI tool understand the task. For instance, if generating a product description, don’t forget to include the product’s key features and the intended target audience.
Create reusable templates. Develop prompt templates for recurring tasks to save time and ensure consistency across teams. For instance, a customer service team could use a standard prompt to handle refund requests or common complaints.
Monitor for bias and accuracy. Regularly test prompts across different scenarios to identify and address any unintended bias or inaccuracies in the AI solution's output.
Use feedback loops. Use the AI’s output to inform and improve future prompts. Where applicable, incorporating feedback from team members or end users can further support continuous improvement.

The future of prompt engineering tools

Prompt engineering is an emerging and rapidly evolving niche that could help to transform how organizations train and use AI.

Prompt engineering is evolving to meet the growing needs of businesses, with tools becoming smarter, easier to use, and better aligned with real-world applications.
As AI adoption grows, industries are beginning to develop standardized approaches to prompt engineering to ensure consistent results across different applications.
Tools for prompt engineering are becoming more intuitive, offering built-in guidance to help users create effective prompts with minimal effort.
Many platforms are exploring real-time feedback tools that help enterprises refine prompts and enhance performance. This may make it easier to adjust AI solutions to growing business demands.
Industry-specific solutions, including pre-built prompt libraries, may make it easier for enterprises to adopt AI tools tailored to their requirements.
Technologies like RAG will likely continue to improve prompt engineering by pulling relevant information from internal and external knowledge bases, making AI outputs more accurate and appropriate to business needs.
Collaborative online communities are emerging for teams to share best practices in prompt engineering and support enterprises in accelerating AI adoption.
New tools to measure prompt effectiveness are being developed. These will provide enterprises with actionable insights to help them enhance outcomes and optimize their enterprise AI ecosystem.

FAQs about prompt engineering

What is an example of using roles in prompt engineering?

Using roles in prompt engineering involves specifying the model’s perspective or identity to guide its response.

For example, a prompt engineer might instruct: “You are an IT manager. Draft an email explaining to staff why a system update is necessary and how it will affect their work.”

This helps the model to generate a response that aligns with an IT manager's communication style and priorities. In turn, this helps to ensure the information is technically correct, addresses potential concerns, and clearly explains the practical benefits of the update.

What is an example of iteration in prompt engineering?

Iteration in prompt engineering implies refining a prompt to improve the final output. For example, suppose the initial prompt “summarize this report” generates a vague response. In that case, you might want to refine it to “summarize this report in three bullet points focusing on key financial metrics” to guide the model in producing a more targeted response.

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What is prompt engineering: Definition and use cases