Discover Prompt Engineering | Google AI Essentials

Published 2024-05-13
This video is a preview of Module 3 in Google AI Essentials, available on Coursera. In this module, you'll write effective prompts to get the output you want. You'll learn how to incorporate prompting techniques, such as few-shot prompting, into your work, and you'll understand how LLMs produce output and the importance of evaluating output before using it. By the end of this module, you will be able to write clear and specific prompts and produce outputs that help accomplish workplace tasks. You'll learn from experts at Google and get essential AI skills to boost your productivity with Google AI Essentials, zero experience required.

In this self-paced course, you’ll gain hands-on experience using generative AI tools to help develop ideas and content, make more informed decisions, and speed up daily work tasks. After you complete the course through Coursera, you’ll earn a certificate from Google to share with your network and potential employers.

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00:00 Module 3 introduction: Discover the art of prompt engineering
3:05 Understand large language models
8:13 Write clear and specific prompts
12:28 Leverage an LLM's capabilities at work
18:10 Improve AI output through iteration
24:03 Discover few-shot prompting
28:26 Wrap-up


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Discover Prompt Engineering | Google AI Essentials
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All Comments (21)
  • @deekana
    The best presenter I have ever listened to. Kudos! Perfect and purposeful use of tone, voice, and body language. Effective delivery. I learned more about presentation skills from this VDO than about prompt engineering lol. The team also did very well with filming, editing and directing. Well done!
  • @capt2026
    Crystal clear presentation. Good job!
  • @LAG455
    Thank you for your teaching ☺️
  • @ailearnershub
    Super'o'Super presentation and explanation and emphasis - Thanks Gentleman...
  • @hmnemonic
    I love his voice…❤ Thanks for your presentation!
  • thanks for this type of knowledge pls update us with type of videos in future
  • @Psrk4287
    What do you do when you don't know what the output should be? How will you use critical reasoning to evaluate the output then?
  • I am against using the word engineering in “prompt engineering”. I understand that coding is undergoing significant changes with the introduction of LLMs such as GPT, however, in my opinion we need to be more precise in choosing words that match the actual task being done by the software developer. Engineering design has a very precise meaning and it incorporates a lot of stages starting from setting quantitative measurable specifications, proposing multiple design alternatives, evaluating them against the specifications, iterative analysis and design usually involving mathematics and physics, selecting a design after examining trade offs among conflicting metrics, implementing the selected design, evaluating the implementation on different platforms (if feasible), module testing, integration testing, and more. Those engineering design stages constitute a cycle that can be revisited with each iteration including even the specifications stage which can be modified if necessary, but with caution and after agreement with the project stakeholders. In that context, software engineering for example might not satisfy all the above while being considered engineering nevertheless, but with some reservation in my opinion. However, prompt engineering barely includes a very small fraction of engineering design stages, and thus in my opinion should be given another name.
  • @bahlechonco211
    i love this this video but why are you crouching? the furniture looks small
  • @krishangopal4808
    Can Gemini and ChatGPT help in writing research paper which can minimise Palagrism?