#chatgpt prompt engineering course

[genzeeacademy]

[businessiideahindi]

Prompt Engineering से पैसे कमाएँ |Unique way to earn money online..

Prompt Engineering : online सफलता के लिए प्रभावी Prompt डिज़ाइन करना | Online संचार के तेजी से विकसित हो रहे परिदृश्य में, Prompt Engineering एक महत्वपूर्ण कौशल के रूप में उभरी है, जो जुड़ाव बढ़ाने, बहुमूल्य जानकारी देने और यहां तक ​​कि डिजिटल इंटरैक्शन का मुद्रीकरण करने का मार्ग प्रशस्त कर रही है। यह लेख Prompt Engineering की दुनिया पर गहराई से प्रकाश डालता है, इसके महत्व, सीखने के सुलभ…

[jcmarchi]

Mastering the Art of AI Prompts: 5 Techniques for Advanced Users

New Post has been published on https://thedigitalinsider.com/mastering-the-art-of-ai-prompts-5-techniques-for-advanced-users/

Mastering the Art of AI Prompts: 5 Techniques for Advanced Users

The use of large language models (LLMs) like ChatGPT is exploding across industries. Even scientists are leaning on AI to write or at least polish their work. A recent analysis of five million scientific studies published in 2023 found a rapid increase in certain words, such as meticulous, intricate, and commendable. The reason for this increase? The use of LLMs. 

AI’s impact is already massive, rapidly transforming the workplace. But we’re still at the early stages, trying to strike a balance between harnessing AI without leaning too much on it and compromising quality. At my company Jotform, we have incorporated AI tools to automate tedious tasks, or as I call it, “busywork,” and free up employees to focus on the meaningful work that only humans can do. I view AI tools like ChatGPT as valuable resources—low-cost, efficient AI assistants. But as anyone who’s used ChatGPT will tell you, it’s not 100% reliable or accurate. And it’s only as effective as the prompts you give it.

I recently asked ChatGPT how to develop your prompt engineering skills. The first response was: “Experimentation and Iteration: Continuously experiment with different types of prompts and refine them based on the AI’s outputs. Track what works and what doesn’t, and iteratively improve your prompts.” 

In other words, practice makes perfect. 

With this in mind, here are my top strategies for taking your prompt writing to the next level. 

Try meta-prompts

Communication is the lynchpin of a successful relationship. Understanding how people communicate and how to best communicate with them can help grow a stronger bond and overcome inevitable misunderstandings when there is a disconnect between words and intentions or emotions. In the same vein, your interactions with ChatGPT can vastly improve if you explicitly ask it how to best communicate with it. I regularly ask ChatGPT how to phrase prompts in order to get the information or feedback I’m seeking. The more information you give ChatGPT about the results you’re after, the better it can help you generate an effective prompt. 

Here’s a sample meta-prompt:

“How would you phrase a prompt to get detailed feedback on an article from ChatGPT? What information would you include to get the most relevant insight?”

In seconds, ChatGPT churned out a prompt template with a brief explanation of why it included each part of the prompt. This kind of insight helps you write better prompts in the future. 

Make it an ongoing dialogue

In most research tools, each query is isolated. You can’t build on a previous search to refine or dig deeper. ChatGPT has the advantage of enabling you to continue a thread, just like a dialogue between two people—a Socratic dialogue, if you will, where you are the teacher and ChatGPT is the student. I find this capability especially useful if I’m new to a subject and would like to begin with a basic level of understanding before diving into the nitty gritty.

To engage in dialogue with ChatGPT, start with a broad prompt and refine the subsequent prompts based on the responses. 

A sequence of prompts might look like this:

“In a few sentences, can you explain the mechanics of a tennis serve?”

“Why does the server shift their weight from the back foot to the front foot?”

“Can you briefly explain kinetic energy transfer?” 

This technique can help you to get more nuanced responses and explore areas and perspectives you might not have otherwise considered. 

Give ChatGPT a reference text

ChatGPT can be a bit of a smooth talker. Ask it about any subject under the sun and it will happily wax poetic for as long as you’d like. The rub: it’s not always accurate. That’s why providing ChatGPT a reference text can help ensure the replies you receive are grounded in reliable sources. 

As OpenAI explains, “In the same way that a sheet of notes can help a student do better on a test, providing reference text to these models can help in answering with fewer fabrications.” Feed ChatGPT the sources that you already know and trust and let the LLM extract the information you need. 

The prompt could be:

“Use the following article in quotes to answer questions.” 

ChatGPT: Sure, please go ahead and provide the articles you’d like me to use for answering your questions.

Then, provide ChatGPT with the article (title and publication will usually do the trick) and your questions. 

Assign ChatGPT a role

One of the most useful, and admittedly fun, capabilities of ChatGPT is to adopt different personas. You probably know the parable of the blind men and the elephant. Each man feels a different part of the elephant and describes the animal based on their limited perspective. As humans, we tend to assume our reality is correct even when it’s based on a limited point of view. Incorporating various perspectives empowers us to understand something more fully. 

By simply instructing ChatGPT to adopt a persona, we can get a new perspective.

Consider these two prompts and personas:

Prompt: Rewrite the following text from the perspective of a poet: A bridge over water connects two lands. 

ChatGPT:

Between shores, where rivers dance and whisper, A bridge, slender and resolute, spans the liquid silver, Uniting distant lands with an arch of promise, A testament to the yearning of hearts and soil.

Prompt: Rewrite the following text from the perspective of an engineer: A bridge over water connects two lands. 

ChatGPT

A bridge over water functions as a structural solution, effectively connecting two separate landmasses. It spans the gap with precision-engineered materials, ensuring stability and durability while facilitating transportation and communication between the two regions.

At Jotform, our marketing team might use this technique to examine an issue from the perspective of different buyer personas. It’s a simple technique with myriad use cases. 

Model the reply you want

While ChatGPT might not be the most reliable narrator, it is extremely logical. Rather than trying to describe the response you want, which can be surprisingly challenging, you can give ChatGPT an example and it will apply the same logic. 

Let’s say I’m working on a marketing campaign and want to use ChatGPT to come up with snappy, compelling copy. 

I could use the following prompt:

Prompt: “I will write words and I’d like you to come up with a corresponding adjective. For example, if I write ‘pretty,’ you might write “more beautiful than the stars in the sky.”

ChatGPT: 

Of course, I’m ready! Please provide the words you’d like me to transform.

Me: nervous

ChatGPT: Nervous as a leaf trembling in the gale of a tempest.

I might consider that on second thought, that’s not the tone I’m after. So I tweak the prompt. 

Prompt: I will write words and I’d like you to come up with a corresponding adjective. For example, if I write ‘pretty,’ you might write “cuter than the prize-winning pig at the Texas State Fair.”

ChatGPT: Got it! Please provide the words you’d like me to transform.

Me: nervous

ChatGPT: Nervous as a cat at a rocking chair convention.

In this case, I’d get an idea for my marketing copy—and a chuckle, at no extra charge.

[harisystems]

What is Prompt Engineer - Prompt Engineer Courses by Open AI - AI - Chat...

[theeuniversesays]

Expert_Juwelier

2 days agoPrompt Engineering is the Alchemy of the Digital Age: turning words into actions, dreams into software. ?

✨ If coding was the job of yesterday, crafting the perfect prompt is tomorrow

#TechTrends #AIRevolution#TechInsights#promptengineering#chatgpt4

[blackjackkent]

Here is the thing that bothers me, as someone who works in tech, about the whole ChatGPT explosion.

The thing that bothers me is that ChatGPT, from a purely abstract point of view, is really fucking cool.

Some of the things it can produce are fucking wild to me; it blows my mind that a piece of technology is able to produce such detailed, varied responses that on the whole fit the prompts they are given. It blows my mind that it has come so far so fast. It is, on an abstract level, SO FUCKING COOL that a computer can make the advanced leaps of logic (because that's all it is, very complex programmed logic, not intelligence in any human sense) required to produce output "in the style of Jane Austen" or "about the care and feeding of prawns" or "in the form of a limerick" or whatever the hell else people dream up for it to do. And fast, too! It's incredible on a technical level, and if it existed in a vacuum I would be so excited to watch it unfold and tinker with it all damn day.

The problem, as it so often is, is that cool stuff does not exist in a vacuum. In this case, it is a computer that (despite the moniker of "artificial intelligence") has no emotional awareness or ethical reasoning capabilities, being used by the whole great tide of humanity, a force that is notoriously complex, notoriously flawed, and more so in bulk.

-----

During my first experiment with a proper ChatGPT interface, I asked it (because I am currently obsessed with GW2) if it could explain HAM tanking to me in an instructional manner. It wrote me a long explanatory chunk of text, explaining that HAM stood for "Heavy Armor Masteries" and telling me how I should go about training and preparing a character with them. It was a very authoritative sounding discussion, with lots of bullet points and even an occasional wiki link Iirc.

The problem of course ("of course", although the GW2 folks who follow me have already spotted it) is that the whole explanation was nonsense. HAM in GW2 player parlance stands for "Heal Alacrity Mechanist". As near as I've been able to discover, "Heavy Armor Masteries" aren't even a thing, in GW2 or anywhere else - although both "Heavy Armor" and "Masteries" are independent concepts in the game.

Fundamentally, I thought, this is VERY bad. People have started relying on ChatGPT for answers to their questions. People are susceptible to authoritative-sounding answers like this. People under the right circumstances would have no reason not to take this as truth when it is not.

But at the same time... how wild, how cool, is it that, given the prompt "HAM tanking" and having no idea what it was except that it involves GW2, the parser was able to formulate a plausible-sounding acronym expansion out of whole cloth? That's extraordinary! If you don't think that's the tightest shit, get out of my face.

----

The problem, I think, is ultimately twofold: capitalism and phrasing.

The phrasing part is simple. Why do we call this "artificial intelligence"? It's a misnomer - there is no intelligence behind the results from ChatGPT. It is ultimately a VERY advanced and complicated search engine, using a vast quantity of source data to calculate an output from an input. Referring to that as "intelligence" gives it credit for an agency, an ability to judge whether its output is appropriate, that it simply does not possess. And given how quickly people are coming to rely on it as a source of truth, that's... irresponsible at best.

The capitalism part...

You hear further stories of the abuses of ChatGPT every day. People, human people with creative minds and things to say and contribute, being squeezed out of roles in favor of a ChatGPT implementation that can sufficiently ("sufficiently" by corporate standards) imitate soul without possessing it. This is not acceptible; the promise of technology is to facilitate the capabilities and happiness of humanity, not to replace it. Companies see the ability to expand their profit margins at the expense of the quality of their output and the humanity of it. They absorb and regurgitate in lesser form the existing work of creators who often didn't consent to contribute to such a system anyway.

Consequently, the more I hear about AI lately, the more hopeful I am that the thing does go bankrupt and collapse, that the ruling goes through where they have to obliterate their data stores and start over from scratch. I think "AI" as a concept needs to be taken away from us until we are responsible enough to use it.

But goddamn. I would love to live in a world where we could just marvel at it, at the things it is able to do *well* and the elegant beauty even of its mistakes.

[bright-and-burning]

okay yeah i didn't know that there's the different ai stuff and that this is generative ai BUT GOD AM I HAPPY to see that you know the difference between ai and ml like !!! this is not ai !!! it's such a trend now and it's so wrong and so frustrating beyond the, in this case, misogynistic aspects of it. like this is not ai !! but that sounds catchy and URGH so frustrating. okay that's all i love ur additional tags thank u xoxo

yeah!! i did my undergrad degree focused on this kind of stuff (and dropped out of doing a phd in it at the last minute) so it's Literally my roman empire. like. i took so many courses in this and THEN it blew up right at the end of my degree and the misinformation........ chatgpt getting massive my senior year of college made me soooo mad i legit started to resent my field. like this particular use is particularly egregious (why hire a woman in an area severely lacking in women when we can rampantly misuse important computational and REAL LIFE resources to create a fake woman to "talk" abt SUSTAINABILITY!!!) but in general i am like constantly infuriated by AI bullshit from a lot of different sides bc Everyone overestimates/glorifies it/turns it into something it isn't, essentially. (rant abt AI from an AI perspective under the cut that idk might be informative or interesting LOL. i tried not to get technical but i did get mad)

generative ai drives me BONKERS bc it's literally not artificial intelligence. chatgpt is NOT AI it's fucking glorified predictive chat and all the dumbass tech bros on linkedin and twitter who hail it as like world changing infuriate me lol. like chatgpt literally works by calculating the word with the highest probability to come next in the sentence/in response to that prompt based on the data it trained on. is that super impressive ignoring the real world stuff going on? yeah!! it is! it's doing really well and it's fascinating in an academic sense. but then you put it in the real world context, where dumbass tech bros and business leaders worship it as god and where SEO morons use it to turn the internet to sludge and it's like oh god WHY.

and the ai art bullshit oh my god. ok i took a machine vision class right. and like literally one of my projects was to write code that could take in a photo and output it in the "style" of another photo. like as the cs version of a creative exercise, basically (they give u a Lot of projects that are basically write ur own version of an algorithm that's already been written more efficiently by someone else, bc that's how you can kind of pick it apart and really understand it, it's like reverse engineering) to show us how that works. (photos from that project are below; i took the cactus photo and then i "combined" it with a monet. i am STAUNCHLY anti-ai "art" btw this was Lich rally an assignment lol). but do you know what i learned (and what my whole class learned)? it's not fucking magic. it's LINEAR ALGEBRA. it's linear algebra on a truly batshit crazy level, combined with some crazy optical physics equations, but it's literally math. technically speaking, if you had infinite time, and were really insanely good at math (and really really perfect abt not making mistakes), you could do it by hand lol.

ai art from prompts? that's just turning words into numbers (not hard!! i did a project that did that in my second year of undergrad in literally 6hrs!!!) and THEN doing some crazy math and turning numbers into PIXELS! the computer does not know what is going on. it is a FANCY CALCULATOR. WHEN U THINK ABT IT LIKE THAT ASSIGNING HUMAN TRAITS TO MATH IS INSANE.

like tech bros who are like bro chatgpt is aliveeee bc they talk to it is like. if i made my high school graphing calculator draw a smiley face and then was like omg it has emotions......

anyways. like do you know how many projects ive worked on that use AI/ML for GOOD??? like. same kind of techniques that ppl use to create generative ai "art" algorithms? used to do things like detect cancer in scans before human doctors can do it with any confidence. isn't that so fucking cool???? i interviewed w a professor who used machine vision (so literally the field that is now seen as being abt ai art) to figure out what nutritional/vitamin deficiencies ppl in remote villages in madagascar are likely to have based on SATELLITE IMAGERY. so that the overworked underfunded public health ministry could more easily meet their needs without necessarily having to do expensive testing on everyone! i mean, shit, i've worked on really cool sports analytics projects using machine vision. that's not exactly saving lives lol but like. just goes to show how many positive applications there are!

in terms of chatgpt vibes like. i've worked on natural language processing! it has so much more potential than spitting out misinformation!! silly projects for classes, like classifying what political party a politician belonged to based on their tweets, but also more serious stuff in the research i did, like analyzing international public opinion by demographic and country on various conflicts based on individuals' social media posts! analyzing covid vaccine opinions based on demographics, and how to encourage vaccination rates based on that!!

idk it's just. infuriating. that ceos and dumbass business majors (sorry to business majors but i have met a Very Specific Type who like to butt into my field and i am Not A Fan) have completely twisted these really and truly interesting projects and applications. to continually make more money and to cut out/replace more and more people. and the way funding has mirrored this kind of interest, in part bc it makes money (the remote villages nutrition thing is never gonna make money. working on chatgpt on the other hand...) and in part bc of like. basically fear-mongering? you get so much more attention for research in these really dramatized fields (and people lie ALL THE TIMMEEEEEEE to get more interest) and it's all CAP. it's BULLSHIT. and it's just to create buzz for big tech companies' bottom lines. they don't care that it's fear based, or whatever. like people being terrified abt the impact of generative ai bc of bullshit headlines and disinformation HELPS THEM make MORE MONEY. and it's so FUCKING STUPID!!!! it makes me SICK!!!!!!

anyways. it's all math, at the end of the day. and i found a job where i get to use it to help people but i still am like deeply grossed out by what comes out of my field and gets popular bc . i got into this bc i loooove data i love information i love finding things out. and i love using those things to help people... and there are ppl using the things i love to hurt people......... it makes me so sad. and mad.

[secondimpact]

lmao there’s a guy running for toronto mayor who thinks chatgpt is a reliable source on sleuthing out corporate, & governmental corruption/collusion because it told him that the wife of the conservative party leader is the ceo of a company that makes covid rapid tests (she’s not btw), and he’s taking the fact that the links that it gave him 404 as proof that the conservative leader had the articles scrubbed.

that is absolutely not how text generators work, they’re not search engines, they will answer your prompts in whatever way they think fits best, and they make false links to legitimate sites as frequently (if not more) as they make legitimate links to legitimate sites. so if you’re going down a line of inquiry, and you’re prompting for something specific of course it’s going to make up shit you wanna hear.

[youzicha]

Cool theory by gwern about the Bing chatbot:

Then you throw in the retrieval stuff, of course. As far as I know, this is the first public case of a powerful LM augmented with live retrieval capabilities to a high-end fast-updating search engine crawling social media*. (All prior cases like ChatGPT or LaMDA were either using precanned web scrapes, or they were kept secret so the search results never contained any information about the LM.) Perhaps we shouldn't be surprised if this sudden recursion leads to some very strange roleplaying & self-fulfilling prophecies as Sydney prompts increasingly fill up with descriptions of Sydney's wackiest samples whenever a user asks Sydney about Sydney... As social media & news amplify the most undesirable Sydney behaviors, that may cause that to happen more often, in a positive feedback loop. Prompts are just a way to fake long-term memory, after all. Something something embodied cognition?

* search engines used to refresh their index on the order of weeks or months, but the rise of social media like Twitter forced search engines to start indexing content in hours, dating back at least to Google's 2010 "Caffeine" update. And selling access to live feeds is a major Twitter (and Reddit, and Wikipedia etc) revenue source because search engines want to show relevant hits about the latest social media thing. (I've been impressed how fast tweets show up when I do searches for context.) Search engines aspire to real-time updates, and will probably get even faster in the future. So any popular Sydney tweet might show up in Bing essentially immediately. Quite a long-term memory to have: your engrams get weighted by virality...

We already complain about the effect Twitter has on the attention span and thinking skills of humans, but I guess it could be even worse for the AIs!

[reasoningdaily]

AI is creating amazing new jobs.

AI has created a field that never existed before — prompt engineering.

To work as a prompt engineer, you must know how to create prompts that generate desirable responses from AI models such as those used by ChatGPT.

The salary isn’t bad.

Artificial intelligence company, Anthropic, has a role for a “prompt engineer and librarian” with a salary range between $175,000 and $335,000.

And that’s not the only company looking for prompt engineers! Let’s see in more detail what prompt engineers do, how you can become a prompt engineer and what are the requirements for this role.

What does a prompt engineer do?

A prompt engineer is someone who develops and refines AI models using prompt engineering techniques. This is like teaching a model how to do something by giving step-by-step instructions or “prompts.”

Prompt engineers work with large language models like GPT-3 (or the new GPT-4), which can generate human-like responses to text prompts. Their work focuses on designing prompts that generate desirable responses from language models, as well as enhancing the models to provide more accurate and relevant text outputs.

Here are some tasks a prompt engineer does:

Optimize language models using established techniques and tools

Write prose to test AI systems for quirks (identify AI’s errors and hidden capabilities)

Review and analyze datasets to identify patterns and trends in language and develop new prompts

Develop and maintain documentation for language models (examples, instructions, best practices, etc)

Train language models on new data sets, and monitor model performance to identify areas for improvement

Collaborate with data scientists/software engineers to integrate language models into software applications and systems.

As you can see, prompt engineers don’t write code all day, but they still need some programming knowledge to be able to work with datasets, develop and fine-tune language models, and collaborate with data scientists and software engineers.

How to learn prompt engineering

You don’t need to know coding to start learning prompt engineering. In fact, in the following link, you can find 4 free prompt engineering courses to join the top 1% of ChatGPT users.

After learning the basics, follow the steps below to continue your path as a prompt engineer.

Learn the basics of programming: As a prompt engineer, you’ll need to work with datasets and understand basic programming concepts. Python can be a good language for this.

Learn natural language processing (NLP) and machine learning (ML) concepts: Prompt engineers need to know concepts in both NLP and ML such as text preprocessing, feature engineering, model training, and optimization.

Practice developing prompts and fine-tuning language models: Learn to use prompt engineering techniques to generate text outputs from language models. Test different prompt types and fine-tune language models to improve performance.

Create a portfolio of prompt engineering projects to showcase your expertise.

The prompt engineering job that pays $335k

Anthropic pays prompt engineers up to $335k. The company specializes in developing general AI systems and language models, which may explain the high salary. It’s no surprise that Google has invested nearly $400 million in this company.

Here are some of the requirements for this job:

Excellent communicator, and love teaching technical concepts and creating high-quality documentation that helps out others

High-level familiarity with the architecture and operation of large language models

Basic programming skills and would be comfortable writing small Python programs

Stay up-to-date and informed by taking an active interest in emerging research and industry trends.

Note that the field of prompt engineering is less than 2 years old, so the prompt engineer role may differ from one company to the next, and, just like any other job, the salary for prompt engineers also varies.

If we compare both job postings, we’ll see that the requirements are different. In the second job posting, they specifically require people with 5 years of engineering/coding experience with at least two-year experience in AI and NLP and a bachelor’s degree in computer science, artificial intelligence, or a related field (healthcare preferred).

Again, the requirements may be higher or lower in some companies, so if you don’t have any experience, a project portfolio is probably the best way to get ahead of other applicants.

Artificial Corner’s Free ChatGPT Cheat Sheet

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[dalanmendonca]

The real challenges of AI

The real challenge of AI is democratisation.

We are in middle of a boom in AI. While it may seem like a sudden explosion in the media; this moment has been building for 15+ years now. From the birth of the internet, to the huge explosion in online information, to algorithmic feeds, then voice assistants, and now at Generative AI/Large language feeds - every invention has grown on the bedrock of technologies before it.

Of course, the fervour we see now is from people experiencing a step change in technology. Till December 2022 you had to write eassy using your own brain and hands; come January 2023 you just provide a small prompt and voila! ChatGPT will write a whole essay for you. WOW.

But we've played this game many times before:

New technology is available

The technology gets hyped and people are promised a better future

Technology gets widely deployed

In the process of societal adoption, it gets co-opted by

Eventually, it gets completely owned by . The new boss is the same as the old boss. The average person is nowhere better.

One interesting aspect of computers is that they are comparatively much more accessible; order(s) of magnitude more accessible.

The steam engine was firmly on the capital side of the equation; it powered everything from trains to factories but it was a while before mechanisation entered the home and became accessible to the average person. Yet overtime we've given up on things like sewing machines and become strictly consumers.

Computing and the internet sit firmly in the middle. Almost by definition - every laptop & smartphone is the "means of production". The entire "internet" however is but a handful of companies; yet because of its inherently decentralised nature; everything from Mastodon to torrents and more can and do exist.

If it wasn't for that design; the internet would like the TV - with much less control in the hands of citizens.

The challenge with AI is to keep it's capabilities as democratically accessibly and user programable as possible; with the internet being the minimum bar. Thankfully with models being open sourced and even run on Raspberry Pi's, we are off to a good start! But we must remain watchful and make sure this happens.

People of my ilk (technologists) are gung ho about applying AI to anything and everything - but this question of democratisation is much fundamental and its effects more pervasive.

[jcmarchi]

MIT faculty, instructors, students experiment with generative AI in teaching and learning

New Post has been published on https://thedigitalinsider.com/mit-faculty-instructors-students-experiment-with-generative-ai-in-teaching-and-learning/

MIT faculty, instructors, students experiment with generative AI in teaching and learning

How can MIT’s community leverage generative AI to support learning and work on campus and beyond?

At MIT’s Festival of Learning 2024, faculty and instructors, students, staff, and alumni exchanged perspectives about the digital tools and innovations they’re experimenting with in the classroom. Panelists agreed that generative AI should be used to scaffold — not replace — learning experiences.

This annual event, co-sponsored by MIT Open Learning and the Office of the Vice Chancellor, celebrates teaching and learning innovations. When introducing new teaching and learning technologies, panelists stressed the importance of iteration and teaching students how to develop critical thinking skills while leveraging technologies like generative AI.

“The Festival of Learning brings the MIT community together to explore and celebrate what we do every day in the classroom,” said Christopher Capozzola, senior associate dean for open learning. “This year’s deep dive into generative AI was reflective and practical — yet another remarkable instance of ‘mind and hand’ here at the Institute.”  

Play video

2024 Festival of Learning: Highlights

Incorporating generative AI into learning experiences 

MIT faculty and instructors aren’t just willing to experiment with generative AI — some believe it’s a necessary tool to prepare students to be competitive in the workforce. “In a future state, we will know how to teach skills with generative AI, but we need to be making iterative steps to get there instead of waiting around,” said Melissa Webster, lecturer in managerial communication at MIT Sloan School of Management. 

Some educators are revisiting their courses’ learning goals and redesigning assignments so students can achieve the desired outcomes in a world with AI. Webster, for example, previously paired written and oral assignments so students would develop ways of thinking. But, she saw an opportunity for teaching experimentation with generative AI. If students are using tools such as ChatGPT to help produce writing, Webster asked, “how do we still get the thinking part in there?”

One of the new assignments Webster developed asked students to generate cover letters through ChatGPT and critique the results from the perspective of future hiring managers. Beyond learning how to refine generative AI prompts to produce better outputs, Webster shared that “students are thinking more about their thinking.” Reviewing their ChatGPT-generated cover letter helped students determine what to say and how to say it, supporting their development of higher-level strategic skills like persuasion and understanding audiences.

Takako Aikawa, senior lecturer at the MIT Global Studies and Languages Section, redesigned a vocabulary exercise to ensure students developed a deeper understanding of the Japanese language, rather than just right or wrong answers. Students compared short sentences written by themselves and by ChatGPT and developed broader vocabulary and grammar patterns beyond the textbook. “This type of activity enhances not only their linguistic skills but stimulates their metacognitive or analytical thinking,” said Aikawa. “They have to think in Japanese for these exercises.”

While these panelists and other Institute faculty and instructors are redesigning their assignments, many MIT undergraduate and graduate students across different academic departments are leveraging generative AI for efficiency: creating presentations, summarizing notes, and quickly retrieving specific ideas from long documents. But this technology can also creatively personalize learning experiences. Its ability to communicate information in different ways allows students with different backgrounds and abilities to adapt course material in a way that’s specific to their particular context. 

Generative AI, for example, can help with student-centered learning at the K-12 level. Joe Diaz, program manager and STEAM educator for MIT pK-12 at Open Learning, encouraged educators to foster learning experiences where the student can take ownership. “Take something that kids care about and they’re passionate about, and they can discern where [generative AI] might not be correct or trustworthy,” said Diaz.

Panelists encouraged educators to think about generative AI in ways that move beyond a course policy statement. When incorporating generative AI into assignments, the key is to be clear about learning goals and open to sharing examples of how generative AI could be used in ways that align with those goals. 

The importance of critical thinking

Although generative AI can have positive impacts on educational experiences, users need to understand why large language models might produce incorrect or biased results. Faculty, instructors, and student panelists emphasized that it’s critical to contextualize how generative AI works. “[Instructors] try to explain what goes on in the back end and that really does help my understanding when reading the answers that I’m getting from ChatGPT or Copilot,” said Joyce Yuan, a senior in computer science. 

Jesse Thaler, professor of physics and director of the National Science Foundation Institute for Artificial Intelligence and Fundamental Interactions, warned about trusting a probabilistic tool to give definitive answers without uncertainty bands. “The interface and the output needs to be of a form that there are these pieces that you can verify or things that you can cross-check,” Thaler said.

When introducing tools like calculators or generative AI, the faculty and instructors on the panel said it’s essential for students to develop critical thinking skills in those particular academic and professional contexts. Computer science courses, for example, could permit students to use ChatGPT for help with their homework if the problem sets are broad enough that generative AI tools wouldn’t capture the full answer. However, introductory students who haven’t developed the understanding of programming concepts need to be able to discern whether the information ChatGPT generated was accurate or not.

Ana Bell, senior lecturer of the Department of Electrical Engineering and Computer Science and MITx digital learning scientist, dedicated one class toward the end of the semester of Course 6.100L (Introduction to Computer Science and Programming Using Python) to teach students how to use ChatGPT for programming questions. She wanted students to understand why setting up generative AI tools with the context for programming problems, inputting as many details as possible, will help achieve the best possible results. “Even after it gives you a response back, you have to be critical about that response,” said Bell. By waiting to introduce ChatGPT until this stage, students were able to look at generative AI’s answers critically because they had spent the semester developing the skills to be able to identify whether problem sets were incorrect or might not work for every case. 

A scaffold for learning experiences

The bottom line from the panelists during the Festival of Learning was that generative AI should provide scaffolding for engaging learning experiences where students can still achieve desired learning goals. The MIT undergraduate and graduate student panelists found it invaluable when educators set expectations for the course about when and how it’s appropriate to use AI tools. Informing students of the learning goals allows them to understand whether generative AI will help or hinder their learning. Student panelists asked for trust that they would use generative AI as a starting point, or treat it like a brainstorming session with a friend for a group project. Faculty and instructor panelists said they will continue iterating their lesson plans to best support student learning and critical thinking. 

Panelists from both sides of the classroom discussed the importance of generative AI users being responsible for the content they produce and avoiding automation bias — trusting the technology’s response implicitly without thinking critically about why it produced that answer and whether it’s accurate. But since generative AI is built by people making design decisions, Thaler told students, “You have power to change the behavior of those tools.”

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Colleagues, the Google AI Essentials program is designed to help people across roles and industries get essential AI skills to boost their productivity, zero experience required. The course is taught by AI experts at Google who are working to make the technology helpful for everyone. In under 10 hours, they’ll do more than teach you about AI — they’ll show you how to actually use it in the real world. Stuck at the beginning of a project? You’ll learn how to use AI tools to generate ideas and content. Planning an event? You’ll use AI tools to help research, organize, and make more informed decisions. Drowning in a flooded inbox? You’ll use AI tools to help speed up those daily work tasks, like drafting email responses. You’ll also learn how to write effective prompts and use AI responsibly by identifying AI’s potential biases and avoiding harm. After you complete the course, you’ll earn a certificate from Google to share with your network and potential employers. By using AI as a helpful collaboration tool, you can set yourself up for success in today’s dynamic workplace — and you don’t even need programming skills to use it. Skill-based modules include: 1) Introduction to AI, 2) Maximize Productivity With AI Tools, 3) Discover the Art of Prompt Engineering, 4) Use AI Responsibly, and 5) Stay Ahead of the AI Curve. Learn generative AI tools to help develop ideas and content, make more informed decisions, and speed up daily work tasks. Write clear and specific prompts to get the output you want - you’ll apply prompting techniques to help summarize, create tag lines, and more. Use AI responsibly by identifying AI’s potential biases and avoiding harm. Develop strategies to stay up-to-date in the emerging landscape of AI. Gain high demand and highly marketable skills in Artificial Intelligence (AI), Prompt Engineering, Large Language Models (LLMs) and Generative AI.

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Download your free AI-ML-DL - Career Transformation Guide.

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2 - “ChatGPT - The Era of Generative Conversational AI Has Begun” (Audible) or (Kindle) 

3 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) (Kindle)

Much career success, Lawrence E. Wilson - AI Academy (share with your team)