What is prompt engineering for text based NN?
Prompt Engineering refers to the process of generating prompts that are used to train text-based neural networks. These prompts can be in the form of questions, statements, or commands and provide the model with valuable information about the task it is being trained on.

The goal of prompt engineering is to create a diverse and representative dataset that will help the model learn the underlying patterns and relationships in the data. This can lead to better performance on both simple and complex tasks.

There are several steps involved in prompt engineering:

    1. Collection of Data: The first step is to collect a large dataset of relevant and high-quality text data. This may involve scraping websites, collecting feedback from users, or using other methods.

    2. Preprocessing: The next step is to preprocess the data to prepare it for training. This includes cleaning and formatting the data, as well as tokenizing it (i.e., breaking it down into individual words or phrases).

    3. Creation of Prompts: Once the data has been prepared, the next step is to generate prompts for use during training. These prompts should be diverse and representative of the types of tasks the model will be expected to perform.

    4. Training: Finally, theprompts are used to train thetext-basedneural network. During this process, themodel is exposed tothe promptsand its responsesare recorded. The model learns to recognize patterns in the data and respond correctly to new inputs.

    5. Evaluating Performance: After training is complete, the model's performance is evaluated. This allows you to identify areas where improvement can be made and adjust your prompt generation strategy accordingly.

Overall, prompt engineering is an important part of the text-based neural network development process. By creating high-quality and diverse datasets, you can help the model learn the necessary skills to perform effectively in a variety of situations.