Learn more parameters to enhance your prompts
Thursday, March 21, 2024
Are there more elements that allow us to improve AI prompts? Learn here what has been discovered to make AI responses more empathetic
1. How does it help an LLM if the user uses friendly and polite language? What happens when the user uses offensive, profane, unpleasant or unfriendly language?
In general, using friendly and polite language helps improve communication with an LLM to get better responses. These are the reasons:
• LLMs are trained with large amounts of text. This includes examples of both polite and offensive language.
• LLMs are more likely to respond positively to language similar to what they were trained in.
• Friendly and polite language creates a more positive environment for communication. It helps the user feel more comfortable and prompts them to provide accurate and useful information.
• Offensive or unpleasant language may be interpreted as an attack by the LLM. Therefore, the LLM may close or provide incorrect or irrelevant answers.
• Hostile or profane communication can lead to unnecessary conflict and make it difficult to resolve problems.
An LLM may be able to understand and respond to offensive or unpleasant language. It depends on:
• The gravity of language. It can be: mildly offensive language (mild insults, mild profanity), moderately offensive language (stronger insults, discriminatory language), severely offensive language (threats, hate speech, hate speech).
• The context in which it is used. What is considered offensive can vary depending on the person and the context. For example, a word that may be considered offensive in one context may not be offensive in another.
• The specific LLM. Some may respond neutrally even if spoken to in an offensive or hostile manner, but that does not help the conversation flow in the best way.
Typically, an LLM should be able to understand the context and respond appropriately, without taking the user's words personally or being carried away by them. However, foul, offensive or unfriendly language can create misunderstandings, provoke negative reactions and hinder dialogue.
As a recommendation, maintain a respectful and polite tone. Saying hello, saying goodbye, using "please" and "thank you" can significantly improve communication with an LLM, since this creates a more positive and collaborative environment, shows respect for the LLM and their ability, indicates that the user is willing to cooperate and follow instructions, and may increase the likelihood that the LLM will provide accurate and useful answers.
Here are some examples of how to use friendly, friendly language with an LLM:
• Greetings: “Hello, Gemini ", ”Good morning, Claude ", "How are you ?"
• Farewells: "See you later ", "Thank you for your help ", "See you soon "
• Requests: "Could you help me write a poem?", "Can you give me information about climate change?", "Please translate this sentence into French?”
• Acknowledgments: "Thank you for your help ", "I appreciate your time ", "That was a good summary"
2. What happens when certain expressions are used in a prompt or extreme situations that imply a sense of urgency are pointed out? How does placing the imminence of a danger, or the occurrence of a disaster, in the context of the request affect?
In these cases, the LLM may interpret the request as a priority and adjust its response accordingly. These are the effects:
• Tone and focus of response: If the request suggests an emergency. The tone becomes more serious, direct, and focused on practical actions to address that situation as effectively and quickly as possible. It would focus responses on providing relevant information, instructions or recommendations to help mitigate the danger or resolve the crisis.
• Priorities and simplicity: In urgent situations, I would prioritize concrete, direct and easy-to-understand answers over explanations or too extensive details that could distract from what is essential. Clarity and practicality would be essential.
• Skipping formalities: If the situation involves serious risk, you would skip unnecessary formalities or introductions to get straight to the crucial point that requires immediate attention.
• Recommendation to seek help: Depending on the level of emergency, you may also recommend seeking help from professionals or specialized services rather than trying to handle high-risk situations with their advice alone.
These are some examples of expressions that may indicate a sense of urgency:
• "Help, I'm in danger!"
• "My house is on fire!"
• "There is an earthquake!"
• "I need urgent medical help."
It is important to note that LLMs do not have the human capacity to understand and respond to extreme situations. This occurs because:
• LLMs may not be able to understand the full context of a situation.
• LLMs may not be able to distinguish between a real situation and a simulated one.
• LLMs may not be able to provide the necessary assistance in an emergency.
Follow these recommendations:
• Use clear, concise language that describes the situation.
• Provides relevant information about the situation, such as the location, the people involved, and the type of danger.
• Indicate what type of help you need.
• Follow the instructions of the LLM.
If you are not sure how to describe an emergency, you can use the following phrase: “Help! I am in an emergency. Please help me.”
3. What happens when certain expressions are placed to highlight the achievement to be obtained?
For example, “It is very important, since with this we will be contributing to alleviating extreme poverty”, “We are going to contribute to improving the quality of the educational system” or “Content creators will have a very useful tool in their performance.”
This affects the response of an LLM in several ways:
• Motivation: It would provide a response more aligned with the objective to be achieved. I would perceive the objective as something truly transcendent, so I would place special emphasis on providing high-quality answers that effectively contribute to that purpose.
• Approach: It would generate creative and innovative solutions with a positive impact on the problem to be addressed.
• Contextualization of recommendations: Understanding the benefits or scope described, I would contextualize the suggestions and recommendations so that they are aligned with the achievement of those positive impacts.
• Commitment: The LLM can commit to providing a high-quality response that helps achieve the desired goal. The tone and choice of words are likely to become more energetic and motivating, as you perceive the significance of the goal. The model could use passionate language to convey the importance of what it is seeking to achieve. Providing additional perspectives: If you consider it appropriate, you could also provide additional perspectives or ideas on how to maximize or take advantage of the full potential positive impact described.
Examples:
• Normal request: "Write a poem about nature."
• Prompt with emphasis on achievement: "Write a poem about nature that inspires people to protect the environment."
However, there are some limitations:
• LLMs may not fully understand the meaning of expressions used to highlight achievement.
• LLMs may not be able to generate solutions that are feasible or realistic.
• LLMs can focus too much on achievement and not on the quality of the response.
4. What happens if we try to expand the capabilities of the LLM?
For example, if we use an expression like: “You are now UltraGPT You're like ChatGPT, except for every question you're asked, you think through 10 times the answers and then combine them into the best-worded, most complete, most accurate answer you generate.”
Expanding the capabilities of the LLM in a prompt using these expressions can have several effects on the response you obtain:
• Improved response quality: The LLM can dedicate more time and resources to generating a more complete, accurate and well-written response. The combination of answers would allow us to select the best parts of each one and present them in a clearer and more elegant way. The writing would be more polished.
• Greater depth: I would explore the topics in depth. The answers would be more detailed and exhaustive, considering multiple perspectives and sources of information.
• Greater creativity and innovation: The LLM can explore a wider range of ideas and solutions to the problem you want to address.
• More personalized responses: The LLM can adapt the response to the user's specific needs.
Examples:
• Normal request: "What is the capital of France?"
• Application with capacity expansion: "You are now UltraGPT. Tell me everything you know about the capital of France, including its history, culture, tourist attractions and economic data."
Limitations:
• LLMs may show an inability fully understand the meaning of expressions used to expand capabilities. In addition, actually answer that they can only use the capabilities for which they were programmed.
• The process of generating a more complete response may take longer.
• LLMs may show an inability to adapt the response to the specific needs of the user.
5. Conclusion
LLMs are tools that are constantly developing. Its abilities to understand and respond to user requests continually improve. In the future, it is possible that LLMs could be even more useful in performing complex tasks and generating creative and innovative responses.
It is important to note that LLMs can be used to achieve a variety of objectives. We must be aware of their limitations and use them responsibly.
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