Course Summary - DeepLearning.AI - ChatGPT Prompt Engineering for Developers

• TL;DR

• Enjoyed learning about the emerging field of Prompt Engineering via this course taught by Andrew Ng ( DeepLearning.AI ) and Isa Fulford ( OpenAI ).

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• Course Link

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• Chapters

• Introduction

• Guidelines

• Iterative

• Summarizing

• Inferring

• Extracting Key topic(s)
• Extracting Sentiment, and Sentiment score(s)
• Executing multiple task(s) with a single prompt

• Transforming

• Language translation
• Inferring a language
• Multiple translations
• Universal translator
• Multiple input and output language(s) are supported
• Tone transformation
• Make some text more / less compelling
• Make some text more / less formal
• Translate across format(s)
• For example JSON to HTML
• Spellcheck / Grammar check

• Expanding

• Temperature input parameter can be used to control the level of randomness of the response.
• For more 'production' grade applications, it is recommended to set this to 0.

• Chatbot
• In this mode we can setup the context for a conversation using a system prompt, and carry it forward.
• Model(s) are stateless, and for carrying out a conversation, all prior context must be provided at the time of the interaction.
• In a few line(s) of a Code, a full-blown Pizza Order Entry Chatbot was written, which accurately interacted with the user and then captured the order.
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• Conclusion

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• Important Terms

• Zero-Shot Learning

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• Key Learning(s)

• With GPT it is possible to have a single model that performs multiple language task(s), in a matter of a few minute(s). 
• Prompt Engineering
• Writing clear and specific instructions is important.
• OPEN AI API can be used for programmatically interfacing with the GPT model(s).
• Delimiters are important to identify different parts of your prompt.
• Output(s) can be easily modified to different format(s).
• JSON Output can be very helpful for ingestion into program(s).
• Learnt following interesting Python Package(s):  
• Redline(s)
• It can be used to programmatically execute diff(s) between two pieces of text, and display it in a visually clean manner. 
• Panel
• Can be used to rapidly spin-up user interface(s), for example, within Jupyter Notebooks. 

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