---Deeplearning.ai , By Andrew Ng---
ChatGPT is a large language model trained by OpenAI based on the GPT-3.5 architecture, designed to generate human-like responses to text-based prompts. ChatGPT can be used for a variety of natural language processing (NLP) tasks, including summarization, translation, and question answering.
Prompt Engineering is the process of creating effective prompts for ChatGPT to generate high-quality responses. It involves iterative, summarizing, transforming, inferring, and expanding techniques.
Iterative prompts involve gradually building up a prompt by adding context to the initial prompt. This helps ChatGPT better understand the intended meaning of the prompt.
Summarizing prompts involve condensing the original prompt into a shorter and more concise format. This can help ChatGPT focus on the most important aspects of the prompt and generate more relevant responses.
Transforming prompts involve modifying the original prompt in some way, such as changing the tense of verbs or replacing synonyms with more common words. This can help ChatGPT understand the prompt more easily and generate more accurate responses.
Inferring prompts involve using prior knowledge or assumptions to provide additional context to the prompt. This can help ChatGPT generate more relevant and accurate responses.
Expanding prompts involve adding additional information or context to the original prompt. This can help ChatGPT generate more detailed and informative responses.
ChatGPT can also be integrated into chatbots, allowing for human-like conversations with users. This can be done using APIs or custom integrations.
ChatGPT Prompt Engineering is an essential tool for developers looking to generate high-quality responses from ChatGPT. By using iterative, summarizing, transforming, inferring, and expanding techniques, developers can create effective prompts and integrate ChatGPT into their applications for more natural language processing.