Prompt Engineering Basics #machinelearning #gpt4 #chatgpt

Automata Learning Lab · Beginner ·🧠 Large Language Models ·3y ago
Skills: Prompt Craft90%

Key Takeaways

The video covers the basics of prompt engineering for Large Language Models (LLMs) using examples with GPT-4 and ChatGPT, highlighting four key elements of a prompt: instructions, examples, input data, and output indicators.

Full Transcript

these are the four elements of a prompt you can give an instruction something like ramulat in two senses about the ocean or write me a sentence about life you can also give examples for example you can say Ramya sent this about life here are a few examples life is fine life is nice the output of the model is gonna be somewhat considering that context you can also give input data to the model and then ask questions about it like in this case I give an abstract and then I ask information about it and this is what I get finally there are output indicators those are probes where you specify the format of the output that you're expecting in this case I'm saying write a sense by Machine learning following the format machine learning is writer quality and is useful for randomly sample application and then the output of the model is machine learning is versatile and it is useful for applications like autonomous vehicle navigation and medical diagnosis and that's it that's some basics of prompt engineering fall for more cheers
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Playlist

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This video teaches the basics of prompt engineering for LLMs, covering the four key elements of a prompt and providing examples to illustrate their usage. By understanding these principles, viewers can improve the performance of LLMs like GPT-4 and ChatGPT. The video is designed for beginners in the field of machine learning and NLP.

Key Takeaways
  1. Identify the four elements of a prompt: instructions, examples, input data, and output indicators
  2. Learn how to craft effective instructions for LLMs
  3. Understand how to provide relevant examples to guide model output
  4. Discover how to use input data to inform model responses
  5. Specify output indicators to control the format of model output
💡 Proper prompt engineering is crucial for achieving desired outcomes with LLMs, and understanding the four key elements of a prompt is essential for effective model usage.

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