The main components of Prompt prompt include instructions, context, input data, and output prompts. Each component is further explained below:

1. Instruction: This section contains explicit instructions or task descriptions for the model. Instructions direct the model to perform a specific operation or answer a specific type of question. Instructions can explicitly ask the model to perform a certain task, such as summarizing an article, answering a specific question, providing advice, etc. The clarity and explicit nature of the instructions is critical for the model to produce accurate answers.

2. Context: The context section provides additional information related to the question or topic to help the model better understand and answer the question. Context can include background knowledge, previous conversational content, relevant facts or events, etc. By providing the appropriate context, the model can generate more accurate and relevant answers based on the specific context of the question.

3. Input Data: Input data are the questions we are interested in or the information we need the model to answer. This part usually takes the form of a question, either a specific problem, an instruction to solve a problem, or a request for relevant information from the model. The accuracy and clarity of the input data is important for the model to generate accurate answers.

4. Output Indicator: The output indicator indicates the form in which we want the model to output results. This can be an instruction to the model to generate a specific type of answer, a request to the model to give reasons or examples, or an instruction to the model to output the results in a certain format or structure. With explicit output hints, we can instruct the model to answer the questions in a particular way and make the answers more structured and readable.

These major components are intertwined to help define the scope of the question, the desired form of the output, and the contextual information needed. Constructing these components rationally and explicitly can guide ChatGPT to generate more accurate and useful responses and meet specific needs.