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Prompt Engineering

Artificial Intelligence
Adopt

Prompt engineering is a process in machine learning, particularly in natural language processing (NLP), where the model is given a prompt or an initial set of instructions to guide its predictions or responses. It's a way to instruct the model about the task it needs to perform.

There are several types of prompt engineering:

  1. Manual Prompt Engineering: This involves manually creating prompts based on the task at hand. For example, for a sentiment analysis task, the prompt could be The sentiment of the text is.

  2. Automatic Prompt Engineering: This involves automatically generating prompts using algorithms. This can be done by training a model to generate prompts based on the input data.

  3. Template-based Prompt Engineering: This involves creating a template for the prompt and filling in the blanks based on the input data. For example, for a text classification task, the template could be Classify the following text: {}.

  4. Data-driven Prompt Engineering: This involves using data to guide the creation of prompts. This can be done by analyzing the input data to identify patterns or trends that can be used to create effective prompts.

  5. Meta Prompt Engineering: This involves creating prompts that instruct the model to generate its own prompts. This can be useful for tasks that require a high level of creativity or flexibility.