Prompt engineering best practices: Tips, tricks, and tools

A range of AI business tools—from Chat GPT to Claude 3—have become cornerstones of the AI revolution, weaving their way into everyday life and business operations. These advanced chatbots are used by individuals and professionals alike to perform various personal and professional tasks. According to DigitalOcean’s bi-annual Currents survey, 45% of respondents acknowledged that AI and machine learning tools have made their job easier. However, there’s a catch: 43% of those surveyed also feel that the effectiveness of these tools is often over-hyped and exaggerated.

This disparity in user satisfaction may be traced to one crucial factor: the art of prompt engineering or the lack thereof. Crafting the right prompt can mean the difference between an AI delivering a valuable output that streamlines your workflow and receiving an inadequate response that leaves you more frustrated than relieved, forcing you to tackle the task independently. As the AI revolution charges forward, the skill of prompt engineering has emerged as a distinct discipline; prompt engineer has become a lucrative and sought-after role within organizations.

This article delves into prompt engineering, providing best practices and a few examples to help your business extract maximum value from these large language models.

What is prompt engineering?

Prompt engineering is the craft of designing and refining inputs (prompts) to elicit the desired output from AI language models. It requires a blend of creativity, understanding of the model’s capabilities, and strategic structuring of the question or statement to guide the AI towards providing accurate, relevant, and useful responses. Prompt engineering improves communication between humans and machines, ensuring the resulting interaction is efficient and effective.

Why is prompt engineering important?

Prompt engineering is crucial because it influences the performance and utility of AI language models. The quality of the input determines the relevance and accuracy of the AI’s response, making prompt engineering a pivotal skill for anyone looking to harness the full potential of these powerful tools. Prompt engineering is not only for prompt engineers. By effectively communicating with AI, anyone can unlock insights, generate ideas, and solve problems more efficiently.

Here are several reasons why prompt engineering is important:

• Improves accuracy: Well-crafted prompts lead to more precise answers, reducing the likelihood of misinterpretation or irrelevant responses from the AI.

• Saves time: Prompt engineering streamlines interactions with the AI by getting the desired information in fewer attempts, saving valuable time for users.

• Facilitates complex tasks: Complex tasks require complex understanding; good prompts translate intricate questions into a form that AI can process effectively.

• Improves user experience: A user’s experience with an AI system can greatly improve when the prompts lead to clear, concise, and contextually appropriate answers.

• Enables better outcomes: In areas such as coding, content creation, and data analysis, well-engineered prompts can lead to higher-quality outcomes by leveraging AI’s capabilities to the fullest.

• Drives innovation: As we better understand how to communicate with AI, we can push the boundaries of what’s possible, leading to innovative applications and solutions.

10 Prompt engineering best practices

Crafting effective prompts for AI can improve the quality and relevance of the responses you receive. This expertise requires a nuanced understanding of how AI interprets and processes natural language inputs. Ahead, we explore ten AI prompt engineering best practices to help you communicate with AI more effectively:

1. Be as specific as possible

Specificity is key to obtaining the most accurate and relevant information from an AI when writing prompts. A specific prompt minimizes ambiguity, allowing the AI to understand the request’s context and nuance, preventing it from providing overly broad or unrelated responses. To achieve this, include as many relevant details as possible without overloading the AI with superfluous information. This balance ensures that the AI has just enough guidance to produce the specific outcome you’re aiming for.

When creating the best prompts for an AI, ask for the following specifics:

• Detailed context: Provide the AI with enough background information to understand the scenario you’re inquiring about. This includes the subject matter, scope, and any relevant constraints.

• Desired format: Clearly specify the format in which you want the information to be presented, whether it’s a list, a detailed report, bullet points, or a summary. Mention any structural preferences, such as headings, subheadings, or paragraph limits.

• Output length: Detail how long you want the AI’s response, whether “3 paragraphs” or “250 words.”

• Level of detail: Indicate the level of detail required for the response, from high-level overviews to in-depth analysis, to ensure the model’s output matches your informational needs.

• Tone and style: Request the preferred tone and style, whether it’s formal, conversational, persuasive, or informational, to make sure the output aligns with your intended audience or purpose.

• Examples and comparisons: Ask the AI to include examples, analogies, or comparisons to clarify complex concepts or make the information more relatable and easily understood.

2. Supply the AI with examples

Incorporating examples into your prompts is a powerful technique to steer the AI’s responses in the desired direction. By providing examples as you write prompts, you set a precedent for the type of information or response you expect. This practice is particularly useful for complex tasks where the desired output might be ambiguous or for creative tasks with more than one correct answer.

When you supply the AI with examples, ensure they represent the quality and style of your desired result. This strategy clarifies your expectations and helps the AI model its responses after the examples provided, leading to more accurate and tailored outputs.

Here are some example types you could provide to an AI to help guide it toward generating the best response possible:

• Sample texts: Share excerpts reflecting the style, tone, and content you want the AI to replicate.

• Data formats: To guide the AI’s output, provide specific data structures, such as table layouts or spreadsheet formats.

• Templates for documents: Offer templates to ensure the AI’s response follows a desired structure and format.

• Code snippets: Provide code examples if you need help with programming tasks to ensure correct syntax and logic.

• Graphs and charts examples: If you’re asking the AI to create similar graphics, share samples of visual data representation.

• Marketing copy: If you’re crafting marketing content, present ad copy that aligns with your brand’s voice for the AI to mimic.

3. Get better answers by providing data

Incorporating specific and relevant data into your prompts significantly enhances the quality of AI-generated responses, providing a solid foundation for the AI to understand the context and craft precise answers. Providing data that includes numerical values, dates, or categories, organized in a clear and structured way, allows for detailed analysis and decision-making. It’s essential to give context to the data and, when possible, to cite its source, lending credibility and clarity to the specific task, whether for quantitative analysis or comparisons.

To ensure the AI delivers the most relevant and insightful answers, always use updated and well-organized information, and if comparisons are needed, establish clear parameters. Supplying the AI with concrete, contextualized data transforms raw figures into intelligible and actionable insights. Data-driven prompts are particularly valuable in tasks requiring a deep dive into numbers, trends, or patterns, enabling the AI to generate outputs that can effectively inform business strategies or research conclusions.

4. Specify your desired output

When engaging with AI, articulate the precise format and structure you expect in the response. Specify whether you require a detailed report, a summary, bullet points, or a narrative form to ensure the AI tailors its output to your needs.

Indicate any preferences such as tone, style, and the inclusion of certain elements like headings or subheadings. By clearly defining your desired output, you guide the AI to deliver information that aligns seamlessly with your intended use.

5. Provide instructions on what to do instead of what not to do

When constructing prompts for AI, it’s more effective to direct the system toward the desired action rather than detailing what it should avoid. This positive instruction approach reduces ambiguity and focuses the AI’s processing power on generating constructive outcomes.

Negative instructions often require the AI to interpret and invert them, increasing the cognitive load and potential for misunderstanding. By clearly stating the intended actions, you enable the AI to apply its capabilities directly to fulfilling the task at hand, improving the efficiency and accuracy of the response.

6. Give the model a persona or frame of reference

Assigning a persona or a specific frame of reference to an AI model can significantly enhance the relevance and precision of its output. By doing so, you get more relevant responses, aligned with a particular perspective or expertise, ensuring that the information provided meets the unique requirements of your query.

This approach is especially beneficial in business contexts where domain-specific knowledge is pivotal, as it guides the AI to utilize a tone and terminology appropriate for the given scenario. The persona also helps set the right expectations and can make interactions with the AI more relatable and engaging for the end user.

7. Try chain of thought prompting

Chain of thought prompting is a technique that elicits a more deliberate and explanatory response from an AI by specifically asking it to detail the reasoning behind its answer. By prompting the AI to articulate the steps it takes to reach a conclusion, users can better understand the logic employed and the reliability of the response.

This approach is particularly useful when tackling complex problems or when the reasoning process itself is as important as the answer. It ensures a deeper level of problem-solving and provides a learning opportunity for the user to see a modeled approach to reasoning.

8. Split complex tasks into simpler ones

When dealing with complex tasks, breaking them into simpler, more manageable components can make them more approachable for an AI. Using step by step instructions helps prevent the AI from becoming overwhelmed and ensures that each part of the task is handled with attention to detail.

Additionally, this approach allows for easier monitoring and adjustment of each step, facilitating better quality control throughout the process. By compartmentalizing tasks, the AI can also use its resources more efficiently, allocating the necessary attention where it’s most needed, resulting in a more effective problem-solving strategy.

9. Understand the model’s shortcomings

In crafting prompts for an AI, recognize the model’s limitations to set realistic expectations. Prompting AI to perform tasks it’s not designed for, such as interacting with external databases or providing real-time updates, will lead to ineffective and potentially misleading outputs called AI hallucinations.

Here are some known shortcomings of AI models:

• Lack of real-time data processing, as the knowledge is up-to-date only until the last training cut-off.

• Inability to access or retrieve personal data unless it has been shared during the interaction.

• No direct interaction with external software, databases, or live web content.

• Potential bias in the data, as AI models can inadvertently learn and replicate biases present in their training data.

• Limited understanding of context can lead to less nuanced responses in complex or ambiguous situations.

• The absence of personal experiences or emotions means the AI cannot form genuine, empathetic connections or offer personal anecdotes.

10. Take an experimental approach to prompting

Prompt engineering is an emergent field that necessitates an experimental mindset. As you navigate this new territory, use an iterative process to test various prompts, paying careful attention to how slight modifications can significantly alter the AI’s responses. You’ll only learn how models respond by testing them.

While maintaining a commitment to AI privacy and ethical standards is key, don’t hesitate to explore diverse phrasings and structures to discover the most effective prompts. This trial-and-error process can yield better results and contribute to a broader understanding of how large language models interpret and act on different types of instructions.

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