What does “prompting” mean?

The term "prompting" in the context of artificial intelligence, especially when using FinanceGPT or large language models (LLMs) like ChatGPT, refers to the process where users provide an input prompt to receive a desired output (e.g. response or action from the model). A prompt can be a question, an instruction, or simply a thematic suggestion that guides the model to respond in a specific way. On the recording platform the prompt will lead to a report.

Examples of Prompting:

1. Question prompt: A user asks, "What are the main causes of climate change?" The model interprets this input as a prompt to generate information about climate change from the information in the transcript.

2. Creative prompt: A user might say, "Write a summary." Here, the model is prompted make a summary.

3. Command prompt: A user might instruct, "Create a list of tasks for project management that where mentioned in the transcript" The model would then generate a corresponding list.

Prompting is thus a central mechanism in interacting with AI-based systems and is crucial for how effectively and efficiently these systems can be utilized.

Why are good prompting and prompting techniques important?

Good prompting and effective prompting techniques are essential for maximizing the utility and accuracy of responses Unique FinanceGPT. Here’s why these techniques are particularly important:

1. Precision in Responses: Good prompts lead to more precise and relevant responses. When a prompt is clear and specific, it guides the AI to understand the exact nature of the information needed, thereby reducing ambiguity and enhancing the accuracy of the response.

2. Efficiency: Well-crafted prompts can reduce the need for follow-up questions by directly addressing the core issue or need in the initial interaction. This makes the conversation more efficient, saving time for both the user and the AI.

3. Contextual Relevance: Effective prompting helps in providing the necessary context that the AI needs to generate appropriate responses. Without sufficient context, responses might be too generic or off-target, which can lead to misunderstandings or irrelevant information being relayed.

4. User Experience: Good prompts improve the overall user experience by making interactions with AI more fluid and less frustrating. Users are more likely to continue using a tool that consistently understands and addresses their needs effectively.

5. Reducing Misinformation: By crafting prompts that are specific and directed, users can help mitigate the risk of the AI generating incorrect or misleading information. This is particularly important in domains where accuracy is critical, such as medical, legal, or financial advice.

6. Leveraging Full Capabilities: Effective prompting techniques allow users to fully leverage the capabilities of AI. For instance, knowing how to prompt for creative writing, technical explanations, or data analysis can vastly expand the utility of AI in various professional and personal contexts.

7. Encouraging Ethical Use: Proper prompting can also guide the ethical use of AI technologies. By framing questions that avoid biases and respect privacy, users contribute to the responsible deployment of AI solutions.

In summary, good prompting is crucial because it directly influences the effectiveness, efficiency, and satisfaction of interactions with Unique FinanceGPT. It ensures that users can extract maximum value from the technology in a reliable and user-friendly manner.

Responsible Prompting

Responsible prompting is important because it contributes to ethical, accurate, and safe interactions when using AI systems like language models. Here are some key reasons why responsible prompting plays an essential role:

Prevention of Misinformation: By carefully formulating prompts, AI models can be guided to generate more precise and reliable information. This is particularly crucial in fields like medicine, law, and science, where accuracy is critical.

Ethics and Fairness: Responsible prompting helps to minimize biases and promote fair responses. Through conscious and ethical prompting, the reinforcement of such biases can be avoided.

Privacy and Confidentiality: Prompts should be designed in a way that they do not reveal sensitive or personal information or request such information from the AI. This protects user privacy and complies with data protection regulations.

User-Friendliness and Accessibility: Clear and understandable prompting allows users of all skill levels to interact effectively with AI systems. This promotes broader accessibility and use of the technology.

Responsible Use: Through responsible prompting, users can be encouraged to use AI systems in a way that is socially responsible and ethically sound. This helps prevent misuse and harmful applications.

Context Sensitivity: Responsible prompting takes into account the context of the request, leading to more appropriate and contextually correct responses. This improves the relevance and usefulness of AI responses for specific situations.

Uniques Guidelines for responsible prompting

Every user is responsible for their own prompts and it should be used in a way to not violate the open Code of conduct for Azure OpenAI Service or Uniques Terms of Service.

Don’t use harmful prompts and make sure to only request ethically responsible reports

Do not use Unique FinanceGPT for performance rating, as it is unreliable.

Harmful prompts are detected by OpenAI, which issues warnings to users.

Unique may exclude users who break guidelines.

Be mindful when asking for sensitive information (e.g. health information, financial statement) when prompting

We prohibit the use of our service for generating content that can inflict harm on individuals or society

Be reminded that as a user you are accountable for final decisions and/or final content

Overall, responsible prompting fosters healthier interaction between humans and AI by ensuring that the technology is used in a manner that is both useful and ethical. It is a crucial component for the responsible deployment of AI in society.

Iterative Process

An iterative process is a method in which an activity is repeated to gradually improve a product or result. In the context of prompting, this means that prompts are continuously adjusted and refined based on the feedback or results they generate.

Why is an iterative process important in prompting?

Improvement of answer quality

By repeatedly testing and adjusting prompts, you can increase the accuracy and relevance of the answers generated by the AI. Each iteration provides an opportunity to clarify misunderstandings, eliminate ambiguities, and improve language precision.

Adaptation to different contexts

A prompt that works well in one context may be less effective in another. Iterative testing allows prompts to be adapted to different use cases and user requirements, which is particularly important when the AI is used in different scenarios or with different target audiences.

Learning and understanding AI dynamics

Through the iterative process, you learn how the AI responds to different formulations and commands. This understanding is crucial for creating more effective prompts that optimize the capabilities of the AI.

Troubleshooting and fine-tuning

Iterative prompting allows for identifying and addressing specific errors or weaknesses in the AI's responses. This is particularly important when the AI's answers influence critical decisions or actions.

Increasing user satisfaction

By iteratively improving prompts, you can ensure that the AI's answers better meet the needs and expectations of users. This leads to higher user satisfaction and acceptance of AI-powered solutions.

Example of an iterative process in prompting

Initial prompt version:

"Tell me something about the client."

Feedback:

The answer was too general and did not consider the desired level of detail.

Improved prompt:

"Describe the financial situation of the customer"

Further feedback:

The answer improved but needed more specific data points.

Further improvement:

"List the key financial situation of the customer that influences her decision on investing.”

Through this iterative approach, the prompt is gradually refined, leading to more accurate and useful AI answers. This process promotes a deep understanding of how specific formulations can influence the quality and relevance of the AI's responses.

Prompting Techniques

Prompting techniques are crucial when using LLM’s because they directly influence how effectively the AI understands and responds to user requests

• On the page Prompting Guide Unique FinanceGPT Chat External Knowledge & Recording , you will find prompting techniques to create good reports from recordings.

• You can use the prompting techniques on the page Prompting Guide Unique FinanceGPT Chat External Knowledge & Recording to create good prompts for external knowledge.

• Additionally, you will find some advanced techniques for creating good prompts when using external know-how here Advanced Prompting Guide Unique FinanceGPT Chat.