Best Engineering Practices for Optimizing Your AI Application Development

In the world of artificial intelligence, getting the most out of large language models (LLMs) hinges on how you design your prompts.

This article will cover some easy-to-follow tips on prompt engineering, like different types of prompts, how to keep costs down, making answers short and sweet, and some techniques for building better prompts.

What Is Prompt and Prompt Engineering in Terms of LLM?

In simple terms, a prompt is what you give to the AI to get a specific response or result. It helps the model understand what you want and guides it in generating answers that make sense.

Prompt engineering, in turn, is about making the right questions or instructions for an AI to get the best answers.

Types of Prompts

When working with LLMs, understanding the different types of prompts can help you guide the model’s responses and achieve better outcomes. Here are the primary types of prompts used:

System Prompts

System prompts are like pre-set instructions that help the AI know how to behave. They include things like background info, rules, or limits to make sure the AI’s responses match what you need. Basically, they set the tone and style of what the AI will say.

User Prompts

User prompts are what people type in to get the AI to do something or give specific info. They can be questions, commands, or just statements, depending on what the user needs. These prompts are key to getting the AI to respond in a useful way.

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Prompt Cost Consideration

When working with LLMs, it’s important to keep an eye on prompt costs. The cost usually depends on how many tokens you use in your input and output.

Tokens are just pieces of text the model processes, so using fewer tokens typically means lower costs. To save money, try to keep your prompts short and clear while still providing enough detail to get accurate responses.

How to Make Answers Concise

In many applications, especially those involving large language models (LLMs), obtaining concise and relevant answers is crucial for efficiency and clarity. Here are some strategies to help you make answers more concise:

Ask Direct Questions

Formulate prompts that are specific and to the point. Direct questions help the model understand exactly what information you’re seeking, which can lead to more focused and brief responses.

Example:

Instead of asking, “Can you tell me about the benefits of a balanced diet?”

Ask, “What are three key benefits of a balanced diet?”

Specify the Desired Length

Clearly set the length of the response you want. This helps the model understand the scope of the answer you’re looking for.

Example:

“Summarize the main points of the article in two sentences.”

“Provide a brief overview of the topic in 50 words or less.”

Use Clear Instructions

Provide explicit instructions on the format or content you want. This includes specifying whether you want a list, a summary, or a brief explanation.

Example:

“List three benefits of regular exercise in bullet points.”

“Give a one-sentence definition of blockchain technology.”

Use Contextual Clues

Provide contextual information that helps narrow down the model’s response. Clear context reduces ambiguity and focuses the model on the relevant aspects of the query.

Example:

“In the context of business management, what are the key strategies for improving team productivity?”

“For a high school essay, what are the essential elements of a persuasive argument?”

Implement JSON (JavaScript Object Notation) Formatting

Request responses in a structured format like JSON if applicable. This approach ensures that the output is organized and concise, making it easier to process and use.

Example:

“Provide the response in JSON format with keys for ‘main points,’ ‘examples,’ and ‘conclusion.’”

“Return the information in a JSON array format with each item being a brief summary of the key topics.”

Use Specific Prompts

Adjust your prompts to elicit short, specific answers rather than open-ended responses. Specific prompts help direct the model to focus on concise answers.

Example:

“What are the three most common symptoms of the flu?”

“Name two key benefits of using cloud computing for small businesses.”

What is “The Golden Prompt”?

“The Golden Prompt” refers to the ideal prompt that consistently produces high-quality, relevant, and accurate responses from an LLM. It is often characterized by being well-structured, clear, and specific.

Finding and refining your golden prompt involves experimenting with different phrasings and formats to determine what works best for your specific application.

Prompt Building Techniques

Building good prompts can boost how well your AI app performs. Here are some key techniques to help you get there:

0-Shot Prompting

0-shot prompting means giving the LLM a task or question with no examples. The model uses what it already knows to come up with an answer. This technique is useful for tasks where the model’s broad understanding is sufficient.

Few-Shot Prompting

Few-shot prompting means giving the AI a few examples of what you’re looking for. This helps it get the hang of what you want and provide better, more accurate answers. It’s great for tasks that need specific patterns or styles.

Chain-of-Thought Prompting

Chain-of-thought prompting means guiding the AI through a series of steps to get to the final answer. It’s helpful for complex tasks that need logical thinking or multiple steps. Breaking things down into smaller steps helps the AI give clearer and more detailed responses.

AUTOMAT Framework

The AUTOMAT framework is a structured approach to prompt engineering that helps in creating better prompts. It decodes as follows:

• Act as a …
• User Persona & Audience
• Targeted Action
• Output Definition
• Mode / Tonality / Style
• Atypical Cases
• Topic Whitelisting

Think of it like scripting for a chatbot. You set up the role it plays, who it talks to, what it needs to achieve, the info it should share, its style of communication, how to deal with tricky situations, and what topics it should cover. This way, your LLM knows exactly what to do and says things as expected.

Conclusion

Getting your prompts right is key to making your AI apps work better and cost less. By knowing the different types of prompts, keeping costs in mind, and using smart techniques, you can improve the answers your AI gives.

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