Generative AI is finally coming into its own as a tool for research, learning, creativity and interaction. The key to a modern generative AI interface is the prompt: the request that users compose to query an AI system in an attempt to elicit a desirable response.
But AI has its limits, and getting the right answer means asking the right question. AI systems lack the insight and intuition to actually understand users’ needs and wants. Prompts require careful wording, proper formatting and clear details. Often, prompts should avoid much of the slang, metaphors and social nuance that humans take for granted in everyday conversation.
Getting the most from a generative AI system requires expertise in creating and manipulating prompts. Here are 12 prompt engineering best practices:
Let’s explore each practice in further detail.
12 tips for better prompts
Creating successful prompts takes knowledge and skill. Prompt engineers need a clear idea of their desired answer or result, as well as a thorough understanding of the AI system’s nuances, interface and limitations.
This is tougher than it might seem. Keep the following guidelines in mind when creating prompts for generative AI tools.
1. Understand the desired outcome
Successful prompt engineering is largely a matter of knowing what questions to ask and how to ask them effectively. But this means nothing if the user doesn’t know what they want in the first place.
Before a user interacts with an AI tool, it’s important to define the goals for the interaction and develop a clear outline of the anticipated results beforehand. Plan it out: Decide what to achieve, what the audience should know and any associated actions that the system must perform.
2. Determine the right format
AI systems can work with simple, direct requests using casual, plain-language sentences. But complex requests will benefit from detailed, carefully structured queries that adhere to a form or format that is consistent with the system’s internal design.
Form and format can differ for each model, and some tools, such as art generators, might have a preferred structure that involves using keywords in predictable locations. For example, the company Kajabi recommends a prompt format similar to the following for its conversational AI assistant, Ama:
“Act like” + “write a” + “define an objective” + “define your ideal format”
A sample prompt for a text project, such as a story or report, might resemble the following:
Act like a history professor who is writing an essay for a college class to provide a detailed background on the Spanish-American War using the style of Mark Twain.
3. Make clear, specific requests
AI is neither psychic nor telepathic; the system can only act based on what it can interpret from a given prompt.
Create clear, explicit and actionable requests. Understand the desired outcome, then work to describe the task the model needs to perform or articulate the question the model needs to answer.
For example, a simple question such as “What time is high tide?” is an ineffective prompt because it lacks essential detail. Tides vary by day and location, so the model would not have nearly enough information to provide a correct answer. A much clearer and more specific query would be: “What times are high tides in Gloucester Harbor, Massachusetts, on March 31, 2025?”
4. Define prompt length
Prompts might be subject to minimum and maximum character counts. Many AI interfaces don’t impose a hard limit, but extremely long prompts can be difficult for AI systems to handle.
AI tools often struggle to parse long prompts due to the complexity involved in organizing and prioritizing the essential elements of a lengthy request. Be aware of any token limitations for a given AI tool, and make the prompt only as long as it needs to be to convey all the required parameters.
5. Split up complex tasks
Step-by-step instructions can ensure that the model handles each aspect of a request. For example, instead of using one prompt to request a model to create a detailed seven-day itinerary to popular travel destinations throughout Europe, split the request into a series of prompts related to top European destination locations, optimal times of year to travel, flight and hotel pricing, and excursion options.
Splitting up complex tasks means more detailed prompts, more accurate output and easier troubleshooting. By monitoring the output of each subtask, prompt engineers can pinpoint where the model might be going awry and adjust accordingly.
6. Choose words with care
Like any computer system, AI tools can be excruciatingly precise in their use of commands and language, including not knowing how to respond to unrecognized commands or language.
The most effective prompts use clear and direct wording. Avoid ambiguity, colorful language, metaphors and slang, all of which can produce unexpected and undesirable results.
However, prompt engineers can sometimes employ ambiguity and other discouraged language with the deliberate goal of provoking unexpected or unpredictable results from a model. This can produce interesting output, as the complexity of many AI systems renders their decision-making processes opaque to the user, or be part of safety testing in AI red teaming.
7. Pose open-ended questions or requests
Generative AI is designed to create. Simple yes-or-no questions are limiting and will likely yield short and uninteresting output.
Posing open-ended questions, in contrast, gives room for much more flexibility in output. For example, a simple prompt such as “Was the American Civil War about states’ rights?” will likely lead to a similarly simple, brief response. However, a more open-ended prompt, such as “Describe the social, economic and political factors that led to the outbreak of the American Civil War,” is far more likely to provoke a comprehensive and detailed answer.
8. Include context
A generative AI tool can frame its output to meet a wide array of goals and expectations, from short, generalized summaries to long, detailed explorations. To make use of this versatility, well-crafted prompts often include context that helps the AI system tailor its output to the user’s intended audience.
For example, if a user simply asks an LLM to explain the three laws of thermodynamics, it’s impossible to predict the length and detail of the output. But adding context can help ensure the output is suitable for the target reader. A prompt such as “Explain the three laws of thermodynamics for third-grade students” will produce a dramatically different level of length and detail compared with “Explain the three laws of thermodynamics for Ph.D.-level physicists.”
9. Provide examples
Another technique to ensure a model’s output meets desired goals is to include examples in prompts. Examples that showcase aspects like quality, style, format or tone can help a model tailor its responses.
Providing examples can be useful for a variety of prompts, such as art creation, data analysis or code development. For example, a prompt could include examples of marketing material to help a model replicate the tone of a specific brand, or it could define how to format output by including an example data table or chart.
10. Set output length goals or limits
Although generative AI is intended to be creative, it’s often wise to include guardrails on factors such as output length. Context elements in prompts might include requesting a simple and concise versus lengthy and detailed response, for example.
Keep in mind, however, that generative AI tools generally can’t adhere to precise word or character limits. This is because natural language processing models such as GPT-4 are trained to predict words based on language patterns and tokens, not exact word or character counts. Thus, LLMs can usually follow approximate guidance such as “Provide a two- or three-sentence response,” but they struggle to precisely quantify characters or words.
11. Avoid conflicting terms and ambiguity
Long and complex prompts sometimes include ambiguous or contradictory terms. For example, a prompt that includes both the words detailed and summary might give the model conflicting information about the expected level of detail and output length.
The most effective prompts use positive language and avoid negative language — in other words, “Do say ‘do,’ and don’t say ‘don’t.'” The logic here is simple: AI models are trained to perform specific tasks, so asking an AI system not to do something is meaningless unless there is a compelling reason to include an exception to a parameter.
12. Use punctuation to clarify complex prompts
Just as humans rely on punctuation to help parse text, AI prompts can also benefit from the judicious use of commas, quotation marks and line breaks to help the system parse and operate on a complex prompt.
Consider the simple elementary school grammar example of “let’s eat Grandma” versus “let’s eat, Grandma.” Prompt engineers should be thoroughly familiar with the formation and formatting of the AI systems they use, which often includes specific recommendations for punctuation.
Additional prompt engineering tips for image generators
The 12 tips covered above are primarily associated with LLMs, such as ChatGPT. However, there is also a growing assortment of generative AI image platforms, which can employ additional prompt elements or parameters in requests.
When working specifically with image generators such as Midjourney and Dall-E, keep the following seven tips in mind:
- Describe the image. Offer some details about the scene — perhaps a cityscape, field or forest — as well as specific information about the subject. When describing people as subjects, be explicit about any relevant physical features you want to include, such as race, age and gender.
- Describe the mood. Include descriptions of actions, expressions and environments — for example, “An elderly woman stands in the rain and cries by a wooded graveside.”
- Describe the aesthetic. Define the overall style desired for the resulting image, such as watercolor, sculpture, digital art or oil painting. You can even describe techniques or artistic styles, such as impressionism.
- Describe the framing. Define how the scene and subject should be framed: dramatic, wide-angle, close-up and so on.
- Describe the lighting. Describe how the scene should be lit using terms such as morning, daylight, evening, darkness, firelight and flashlight. All these factors can affect light and shadow.
- Describe the coloring. Denote how the scene should use color with descriptors such as saturated or muted.
- Describe the level of realism. AI art renderings can range from abstract to cartoonish to photorealistic. Be sure to denote the desired level of realism for the resulting image.
Avoiding common prompt engineering mistakes
Prompt formation and prompt engineering can be more of an art than a science. Subtle differences in prompt format, structure and content can profoundly affect AI responses. Even nuances in how AI models are trained can result in different outputs.
The following are several common prompt engineering mistakes to avoid:
- Don’t be afraid to test and revise. Prompts are never one-and-done efforts. AI systems such as art generators can require enormous attention to detail. Be prepared to adjust and take multiple attempts to build the ideal prompt.
- Don’t look for short answers. Generative AI is designed to be creative, so form prompts that make the best use of the AI system’s capabilities. Avoid prompts that look for short or one-word answers; generative AI is far more useful when prompts are open-ended.
- Don’t stick to the default temperature. Many generative AI tools incorporate a temperature setting that, in simple terms, controls the AI’s creativity. Based on the specific query, try adjusting the temperature parameters: higher to be more random and diverse, or lower to be narrower and more focused.
- Don’t use the same sequence in each prompt. Prompts can be complex queries with many different elements. The order in which instructions and information are assembled into a prompt can affect the output by changing how the AI parses and interprets the prompt. Try switching up the structure of prompts to elicit different responses.
- Don’t take the same approach for every AI system. Because different models have different purposes and areas of expertise, posing the same prompt to different AI tools can produce significantly different results. Tailor prompts to the model’s unique strengths. In some cases, additional training, such as introducing new data or more focused feedback, might be necessary to refine the AI’s responses.
- Don’t forget that AI can be wrong. Model responses can be wrong, incomplete or simply made-up, a phenomenon known as hallucination. Always fact-check AI output for inaccurate, misleading, biased, or plausible yet incorrect information.
Editor’s note: This tip was originally written in 2023. Informa TechTarget Editorial updated it in 2025 with relevant technology information to improve readability.
Stephen J. Bigelow, senior technology editor at TechTarget, has more than 30 years of technical writing experience in the PC and technology industry
