AI Prompts: Best Practices

Chain of Thought Prompting (CoT) is a technique in artificial intelligence that enhances the reasoning capabilities of large language models (LLMs).

In the context of advanced prompting, role-playing refers to assigning a specific role or character to an AI model, such as ChatGPT, and instructing it to respond from that perspective.

Persona-based prompting is a technique in advanced prompting where you provide the AI model with a detailed description of a specific persona or character.

Specifying the output format in a prompt means providing clear instructions to the AI model about how you want the response to be structured or presented.

The flipped interaction pattern prompt is a technique used in interacting with language models like myself. Instead of the user providing all the information upfront, the language model leads the conversation by asking clarifying questions.

The Question Refinement Pattern is a prompt engineering technique used to improve the quality of responses from language models like myself. It involves asking the model to suggest a better, more refined version of the original question before providing an answer.

The Reflection Pattern Prompt is a technique used in prompt engineering to improve the quality and reliability of responses from AI models like ChatGPT. It involves asking the model to explain the reasoning, assumptions, and limitations behind its answer, as well as to provide evidence or examples to support its response.

Few-Shot Learning (FSL) is a type of machine learning approach where a model is trained to recognize patterns or make decisions with a very small amount of labeled training data.