Prompt Engineering

I'm thrilled to share that I've learnt chatgpt prompt engineering. While learning this I've explored many things and want to share with you all. Few are below

In ChatGPT Prompt Engineering for Developers, I learnt how to use a large language model (LLM) to quickly build new and powerful applications.? Using the OpenAI API, quickly build capabilities that learn to innovate and create value in ways that were cost-prohibitive, highly technical, or simply impossible before now. This short course taught by Isa Fulford (OpenAI) and Andrew Ng (DeepLearning.AI) described how LLMs work, provided best practices for prompt engineering, and showed how LLM APIs can be used in applications for a variety of tasks, including:

• Summarizing (e.g., summarizing user reviews for brevity)

• Inferring (e.g., sentiment classification, topic extraction)

• Transforming text (e.g., translation, spelling & grammar correction)

• Expanding (e.g., automatically writing emails)

In addition, I learnt two key principles for writing effective prompts, how to systematically engineer good prompts, and also learnt to build a custom chatbot. All concepts are illustrated with numerous examples, which we can play with directly in the Jupyter notebook environment to get hands-on experience with prompt engineering.

#chatgpt #deeplearningai

#prompts #chatgptprompt

#promptengineering #handsonexperience

#jupyter