Discover Prompt Engineering | Google AI Essentials

Показать описание

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.

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

#GoogleCareerCertificates #AI #GenerativeAI

About the Google Career Certificates program:

Google Career Essentials help you build in-demand skills to grow your career. Whether you’re looking to add a skill to your resume, apply a new skill to uplevel your work, or simply learn something new, the course will help you quickly gain valuable competencies. At the end, you’ll earn a certificate to showcase to employers.

Google Career Certificates prepare you for entry-level jobs and take about three to six months to complete. At the end, you’ll unlock job search support including 1-on-1 career coaching, an exclusive job board with 150+ employers, and more.

Google Career Certificates

Discover Prompt Engineering | Google AI Essentials

Рекомендации по теме

Комментарии

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!

deekana

Table of Contents
- [00:00] Introduction to Prompt Engineering
- Prompt engineering is designing prompts to get the desired output from conversational AI tools.
- Prompt phrasing can influence the AI model's response, similar to how we use language in daily interactions.
- [01:09] Motivation for Prompt Engineering
- Yufeng, a Google engineer, was motivated by the inefficiency of getting useful responses from language models.
- The goal is to improve the efficiency of conversational AI tools.
- [03:00] Understanding LLMs for Effective Prompt Engineering
- Understanding how LLMs work and their limitations is crucial for effective prompt engineering.
- LLMs are AI models trained on massive amounts of text to identify patterns and generate responses to prompts.
- [04:01] How LLMs Predict Words and Generate Text
- LLMs are trained on vast amounts of text data to learn language patterns and predict the next word in a sequence.
- They analyze probabilities to choose the most likely word based on the context, but may vary in their responses.
- [04:57] ⚠ Limitations of LLMs: Bias and Unexpected Outputs
- LLM outputs can be biased due to biases present in their training data.
- You may not always get the desired output because LLMs rely on statistical analysis of training data.
- [05:26] ⚠ Limitations of LLMs: Bias and Unexpected Outputs (continued)
- LLM outputs can be factually inaccurate due to limitations in training data or its analysis methods (hallucinations).
- Examples of hallucinations include generating incorrect historical data or company information.
- [06:54] Critical Evaluation of LLM Outputs
- It's crucial to critically evaluate LLM outputs for factual accuracy, bias, relevance, and information sufficiency.
- This applies to all LLM outputs, regardless of the task (summarization, marketing ideas, project plans).
- [08:22] Prompt Engineering for Effective LLM Use
- The quality of the prompt affects the quality of the LLM output, similar to how high-quality ingredients affect a dish.
- Prompt engineering involves designing clear, specific prompts with relevant context to get the desired output from an LLM.
- [10:23] The Importance of Context in Prompt Engineering
- Providing clear and specific context in prompts is crucial for getting the desired output from LLMs.
- Example: A prompt about a professional conference will lead to different results than a prompt about a party.
- [11:25] Prompt Engineering as an Iterative Process
- Even well-designed prompts may require iteration to achieve the best results from LLMs.
- The first attempt may not be perfect, so revising the prompt based on the initial output can be necessary.
- [12:21] LLMs for Content Creation and Summarization Tasks
- LLMs can be used to create content (articles, outlines, emails) and summarize lengthy documents.
- Provide clear instructions and specify the desired task (create, summarize) in the prompt.
- Example prompts are given for creating an article outline and summarizing a paragraph.
- [14:24] LLMs for Text Classification and Data Extraction
- LLMs can be used to classify text data (e.g., sentiment analysis of reviews) and extract information from text to a structured format (e.g., creating tables from reports).
- Examples are provided for classifying customer reviews and extracting city and revenue data from a report.
- [15:25] LLMs for Translation and Text Editing
- LLMs can translate text between languages and edit text for tone, style, and grammar.
- Examples are given for translating a training session title and changing the tone of a technical analysis.
- [18:22] Iterative Prompt Engineering for Optimal Output
- Prompt engineering is an iterative process, similar to developing a product or presentation.
- It often requires multiple tries and revisions to the prompt before achieving the desired LLM output.
- Don't be discouraged by initial failures; instead, evaluate the output and revise the prompt accordingly.
- [19:19] Iterative Prompt Improvement for Better LLM Output
- Getting the best output from an LLM often requires iteration on the prompt.
- Evaluate the output after each attempt and revise the prompt to address shortcomings.
- Reasons to revise the prompt include missing context, inaccurate information, or unsuitable formatting.
- [21:42] Refining Prompts for Well-Structured Output
- LLMs can be instructed to deliver output in a specific format (e.g., table).
- Use clear and specific language to indicate the desired format in the prompt.
- The example focuses on creating a well-organized table with relevant college information.
- [24:11] Using Examples to Improve Prompt Efficiency
- Including examples (or "shots") in prompts can significantly improve the quality of LLM output.
- There are different prompting techniques based on the number of examples provided: zero-shot, one-shot, and few-shot.
- Zero-shot prompts give no examples, while few-shot prompts provide two or more examples to guide the LLM.
- [26:06] Using Few-Shot Prompting to Achieve Specific Style or Format
- Few-shot prompting provides multiple examples (2+) to guide the LLM towards a desired output style or format.
- The prompt should specify the number of adjectives, sentence length, and overall style desired.
- The example focuses on creating a product description that matches the style of previous examples.

AIRecruiterTraining

Crystal clear presentation. Good job!

capt

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.

mahmoudabdelazizabdelaziz

I love his voice…❤ Thanks for your presentation!

hmnemonic

Super'o'Super presentation and explanation and emphasis - Thanks Gentleman...

ailearnershub

thanks for this type of knowledge pls update us with type of videos in future

Footballfantastic

i love this this video but why are you crouching? the furniture looks small

bahlechonco

Good i description & details on how LLlM can be.. Thank You...

prashantsawant

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?

Psrk

Can Gemini and ChatGPT help in writing research paper which can minimise Palagrism?

krishangopal

There is some glitch in the Google map can you guys fix that?

begeshgangadharan

"Shot" is synonymous w/ "example." 0-shot prompts provide no examples for the LLM. Non-zero shot prompts are a basic method to fine-tune LLM output, getting less generalized results.

chrisbrown