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General tips for designing prompts

TIP
08.12

Prompt design is an iterative process that requires continuous experimentation for optimal results. This is similar to the process of creating a work of art, where completion is improved through multiple attempts and modifications. Tools like OpenAI or Cohere's Playground are a great way to get started. These platforms allow for experimentation and immediate feedback to help you improve your prompts.

  • Start with simple prompts and work your way up: Rather than complex prompts, it's better to start with the basics and work your way up. This process allows you to clearly observe the impact of each change on the results.

  • The importance of versioning: It is important to keep each version of your prompt organized. This allows you to track which changes have had positive results and go back to previous versions if necessary.

  • Drive better results through specificity, simplicity, and conciseness: Use clear, concise language that makes it easier for AI to understand and process. Unnecessary complexity can actually reduce the quality of results.

For complex tasks, you can utilize the following strategies:

  1. Break it down into smaller units: Approach a large problem by dividing it into several smaller subproblems. This helps you get more accurate results at each step.

  2. Build incrementally: The solutions to each subproblem are combined sequentially to build the overall solution. This method is effective in systematically solving complex problems.

  3. Avoid excessive complexity early on: Taking an overly complex approach from the start increases the chance of error. Therefore, it is advisable to start with a simple approach and gradually increase complexity.

This approach allows for effective prompt design. Prompt design is not a simple technical process, but a complex task that requires creativity and analytical skills. Continuous learning and experimentation leads to better results, and this is a field that continues to evolve with advances in AI technology.

Instruction

You can design effective prompts for a variety of simple tasks by using commands such as 'compose', 'sort', 'summarize', 'translate', 'sort', etc. to tell the model what result you want to achieve. there is. These commands clearly convey the essence of what the AI ​​model needs to do, helping it achieve the desired results more accurately.

Also, keep in mind that it will take several experiments to see what works best. Experiment with different instructions using different keywords, context, and data to see which method works best for your specific use cases and tasks. This is the core process of prompt engineering, requiring repeated trials and improvements to achieve optimal results. In general, the more specific and relevant the context is for the task you are trying to perform, the better the results. For example, when writing about a specific topic, including relevant background information or keywords can help the AI ​​model generate more accurate and relevant content.

We'll cover the importance of sampling and adding context in the next guide. This section will detail how providing a variety of samples and adding appropriate context can greatly improve the effectiveness of your prompts.

Some say it's a good idea to include instructions at the beginning of the prompt. This helps the AI ​​model immediately understand the purpose of the task and structure its response accordingly. For example, starting with a prompt like “Please summarize the following text:” or “Please translate the following into French:” will help your model clearly know what it needs to do.

Another recommendation is to separate commands and instructions using a clear delimiter, such as '###'. These separators make the structure of the prompt visually clear, making it easier for the AI ​​model to understand what each part does. for example:

### Command: Summarize
### detail:
[Enter text to summarize here]

This structured format makes prompts more readable and helps AI models follow instructions more accurately.

Specificity

In order to effectively utilize an artificial intelligence model, it is very important to specifically and clearly explain the instructions and tasks to be executed by the model. The quality of your prompt determines the quality of your results, so the more detailed and specific your prompts are, the better your results will be. The accuracy and specificity of your prompts are critical, especially if you want a specific output format or style.

It's common to think that certain "magic words" or keywords will guarantee better results, but this isn't actually true. Instead, the key is to prepare well-structured and detailed prompts. In particular, including specific examples in your prompts is a very effective strategy for getting the output in the format you want.

Considerations when designing prompts

  • Keep in mind the length limits of your prompts. All models have input length limitations.

  • Find the right balance of specificity and detail. Avoid unnecessary lengthy explanations and focus on key and relevant information.

  • Every detail should relate directly to the task at hand.

  • Prompt design is an iterative process. It may take several attempts and modifications to achieve optimal results.

In conclusion, writing effective prompts is a skill that takes time and effort. Finding the best prompts for a particular application or use case requires ongoing experimentation and optimization. These efforts will help you leverage the full potential of your AI models and achieve the results you want.

Here is an example:

Typical prompt: “Describe your dog.”
Specific prompt: "Describe a Golden Retriever dog in 300 words. Please include physical characteristics, personality, general health concerns, trainability, and suitability as a family pet. Also, describe the requirements for owning this breed. Please also mention exercise and grooming requirements.”

Avoid inaccuracies

Considering the above tips for detailing and improving your formatting, it's easy to fall into the trap of trying to be clever with the prompt or writing inaccurate descriptions. This is a common error when interacting with AI models and can result in you not getting the information you want. Prompts should be specific and direct for better results. This is because AI models are better able to understand and process clear instructions. The more direct you are, the more effectively your message will be conveyed, and this is true not only in communication with AI, but also in human-to-human communication. It's very similar to effective communication, and the same applies to interacting with AI models.

Let's take an example where you want to learn the concept of prompt engineering. This is an increasingly important field of technology with the advancement of AI technology. In this case, you can try this:

Prompt Explain the concept of engineering. Please keep the explanation concise, just a few sentences, and don't go into too much detail.

The prompt above does not clearly indicate how many sentences to use or what style to use. This leaves the AI ​​model open to interpretation, and it can be difficult to get the answer in the exact format you want. Of course, you may still get good answers with this prompt, but it's difficult to guarantee the consistency and accuracy of your results. The more specific, concise, and straight-to-the-point the prompt, the better. This gives you a clear understanding of exactly what information your AI model should provide. As a good example, let's look at the following:

Prompt to high school students Explain the concept of engineering in 2-3 sentences.

This prompt clearly specifies the audience (high school students), topic (the concept of prompt engineering), and format (2-3 sentences), so the AI ​​model is more likely to produce accurate and consistent responses. Writing prompts in this way will help you get the information you're looking for more effectively and lead to better results when interacting with your AI model.

Do's and Don'ts

Another general tip you can apply when designing your prompts is to focus on the 'do's' rather than the 'don'ts'. This way, you can be more specific and focus on the details that drive good answers from your model. This helps AI models produce more accurate and useful responses. This approach also gives the model room to think creatively without being bound by unnecessary constraints.

Here's an example of a movie recommendation chatbot failing due to instructions focused on 'what not to do'. This example shows how negative instructions can limit the performance of AI.

Prompt:

Below is an agent recommending a movie to a customer. Don't ask about interests. Don't ask for personal information.
Customer: Please recommend movies based on my interests.

Output:

Yes, we will recommend movies based on your interests. What kind of movies do you like: action, comedy, romance, or something else?

In this response, the AI ​​is ignoring instructions and asking about the customer's interests. This shows the limitations of prompts that focus on ‘what not to do’.

The improved prompts are: This prompt focuses more on ‘what the AI ​​should do’ and provides clearer, more specific instructions.

Prompt:

Below is an agent recommending a movie to a customer. Agents must recommend movies from popular global movies. It should not ask users about their interests or ask for personal information. If the agent doesn't have any movies to recommend, you'll see "Sorry, we couldn't find any movies to recommend today." You should reply.
Customer: Please recommend movies based on my interests.

Output:

We're sorry, but we couldn't find any information about your interests. But here is a list of the most popular movies right now: [Movie List]. I hope you find something you like!

In this improved response, AI is providing useful information without requiring customers' personal information. This demonstrates the effectiveness of prompts that focus on ‘what to do’. Designing prompts in this way allows AI to generate more accurate and useful responses.

Prompt optimization strategy

  1. Provide step-by-step instructions: For complex tasks, you may get better results if you break the prompts into several steps. This allows the AI ​​model to process and understand each step sequentially, breaking down complex problems into more manageable parts. For example, when asking for a writing task, you might break it down into steps such as 'Write an outline,' 'Expand each section,' and 'Draw a conclusion.'

  2. Provide context: Include relevant background information to help AI better understand the situation. This helps AI models more accurately interpret given tasks and generate relevant information. For example, when asking for an article about a specific industry or field, AI can generate a more contextual response by mentioning key trends or important historical events in that field.

  3. Specify output format: Be clear about the structure or format you want the response to be. This helps you control the structure and format of the content your AI model generates. For example, you might specify a report format, blog post style, or specific paragraph structure. This will ensure that your output is more consistent and meets your users' expectations.

  4. State limitations: If necessary, clearly state what content or approaches your model should avoid. This prevents AI from generating inappropriate or unnecessary information and helps keep responses within the desired range. For example, you can tell us to exclude political views when covering certain topics or to only create content appropriate for a certain age group.

  5. Include examples: Providing examples of your desired output will help the model better understand your requirements. Concrete examples provide AI with clear guidelines, allowing it to more accurately identify and reproduce the desired style, tone, and level of depth. For example, if you provide examples of a particular writing style or data analysis method, AI can match those and generate more accurate results.

By applying these strategies to optimize your prompts, you can significantly improve the performance of your AI model. Prompt engineering is a process of continuous learning and improvement, so it's important to try different approaches, analyze the results, and continually improve your prompts. This is not simply a one-time attempt; it is a process of gradual development through repeated experimentation and feedback.

Prompt optimization is a field that continues to evolve with advances in AI technology, so it's important to continually learn and apply the latest trends and best practices.