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This next video is on inferring. I like to think of these tasks where the model takes a text as input and performs some kind of analysis. So this could be extracting labels, extracting names, kind of understanding the sentiment of a text, that kind of thing. So if you want to extract a sentiment, positive or negative, of a piece of text, in the traditional machine learning workflow, you'd have to collect the label data set, train a model, figure out how to deploy the model somewhere in the cloud and make inferences. And that could work pretty well, but it was, you know, just a lot of work to go through that process. And also for every task, such as sentiment versus extracting names versus something else, you have to train and deploy a separate model. One of the really nice things about large language model is that for many tasks like these, you can just write a prompt and have it start generating results pretty much right away. And that gives tremendous speed in terms of application development. And you can also just use one model, one API to do many different tasks rather than needing to figure out how to train and deploy a lot of different models. And so with that, let's jump into the code to see how you can take advantage of this. So here's our usual starter code. I'll just run that. And the most fitting example I'm going to use is a review for a lamp. So, "Needed a nice lamp for the bedroom and this one had additional storage" and so on. So, let me write a prompt to classify the sentiment of this. And if I want the system to tell me, you know, what is the sentiment. I can just write, "What is the sentiment of the following product review" with the usual delimiter and the review text and so on and let's run that. And this says, "The sentiment of the product review is positive.", which is actually, seems pretty right. This lamp isn't perfect, but this customer seems pretty happy. Seems to be a great company that cares about the customers and products. I think positive sentiment seems to be the right answer. Now this prints out the entire sentence, "The sentiment of the product review is positive." If you wanted to give a more concise response to make it easier for post-processing, I can take this prompt and add another instruction to give you answers to a single word, either positive or negative. So it just prints out positive like this, which makes it easier for a piece of text to take this output and process it and do something with it. Let's look at another prompt, again still using the lamp review. Here, I have ,it "Identify a list of emotions that the writer of the following review is expressing. Include no more than five items in this list." So, large language models are pretty good at extracting specific things out of a piece of text. In this case, we're expressing the emotions and this could be useful for understanding how your customers think about a particular product. For a lot of customer support organizations, it's important to understand if a particular user is extremely upset. So, you might have a different classification problem like this, "Is the writer of the following review expressing anger?". Because if someone is really angry, it might merit paying extra attention to have a customer review, to have customer support or customer success, reach out to figure what's going on and make things right for the customer. In this case, the customer is not angry. And notice that with supervised learning, if I had wanted to build all of these classifiers, there's no way I would have been able to do this with supervised learning in the just a few minutes that you saw me do so in this video. I'd encourage you to pause this video and try changing some of these prompts. Maybe ask if the customer is expressing delight or ask if there are any missing parts and see if you can a prompt to make different inferences about this lamp review. Let me show some more things that you can do with this system, specifically extracting richer information from a customer review. So, information extraction is the part of NLP, of Natural Language Processing, that relates to taking a piece of text and extracting certain things that you want to know from the text. So in this prompt, I'm asking it to identify the following items, the item purchase, and the name of the company that made the item. Again, if you are trying to summarize many reviews from an online shopping e-commerce website, it might be useful for your large collection of reviews to figure out what were the items, who made the item, figure out positive and negative sentiment, to track trends about positive or negative sentiment for specific items or for specific manufacturers. And in this example, I'm going to ask it to format your response as a JSON object with "Item" and "Brand" as the keys. And so if I do that, it says the item is a lamp, the brand is Lumina, and you can easily load this into the Python dictionary to then do additional processing on this output. In the examples we've gone through, you saw how to write a prompt to recognize the sentiment, figure out if someone is angry, and then also extract the item and the brand. One way to extract all of this information would be to use three or four prompts and call "get_completion", you know, three times or four times to extract these different views one at a time. But it turns out you can actually write a single prompt to extract all of this information at the same time. So let's say "identify the following items, extract sentiment, is the reviewer expressing anger, item purchased, company that made it". And then here I'm also going to tell it to format the anger value as a boolean value, and let me run that. And this outputs a JSON where sentiment is positive, anger, and then no quotes around false because it asked it to just output it as a boolean value. Extracted the item as "lamp with additional storage" instead of lamp, seems okay. But this way you can extract multiple fields out of a piece of text with just a single prompt. And, as usual, please feel free to pause the video and play with different variations on this yourself. Or maybe even try typing in a totally different review to see if you can still extract these things accurately. Now, one of the cool applications I've seen of large language models is inferring topics. Given a long piece of text, you know, what is this piece of text about? What are the topics? Here's a fictitious newspaper article about how government workers feel about the agency they work for. So, the recent survey conducted by government, you know, and so on. "Results revealed that NASA was a popular department with a high satisfaction rating." I am a fan of NASA, I love the work they do, but this is a fictitious article. And so, given an article like this, we can ask it, with this prompt, to determine five topics that are being discussed in the following text. Let's make each item one or two words long, for my response, in a comma-separated list. And so, if we run that, you know, we get this article. It's about a government survey, it's about job satisfaction, it's about NASA, and so on. So, overall, I think, pretty nice extraction of a list of topics. And, of course, you can also, you know, split it so you get a Python list with the five topics that this article was about. And if you have a collection of articles and extract topics, you can then also use a large language model to help you index into different topics. So, let me use a slightly different topic list. Let's say that we're a news website or something, and, you know, these are the topics we track. "NASA, local government, engineering, employee satisfaction, federal government". And let's say you want to figure out, given a news article, which of these topics are covered in that news article. So, here's a prompt that I can use. I'm going to say, determine whether each item in the final list of topics is a topic in the text below. Give your answer as a list of 0 or 1 for each topic. And so, great. So, this is the same story text as before. So, this thing is a story. It is about NASA. It's not about local government. It's not about engineering. It is about employee satisfaction, and it is about federal government. So, with this, in machine learning, this is sometimes called a "Zero-Shot Learning Algorithm", because we didn't give it any training data that was labeled, so that's Zero-Shot. And with just a prompt, it was able to determine which of these topics are covered in that news article. And so, if you want to generate a news alert, say, so that process news, and I really like a lot of work that NASA does. So, if you want to build a system that can take this, put this information into a dictionary, and whenever NASA news pops up, print "ALERT: New NASA story!", they can use this to very quickly take any article, figure out what topics it is about, and if the topic includes NASA, have it print out "ALERT: New NASA story!". Oh, just one thing. I use this topic dictionary down here. This prompt that I use up here isn't very robust. If I wanted a production system, I would probably have it output the answer in JSON format, rather than as a list, because the output of the large language model can be a little bit inconsistent. So, this is actually a pretty brittle piece of code. But if you want, when you're done watching this video, feel free to see if you can figure out how to modify this prompt, to have it output JSON instead of a list like this, and then have a more robust way to tell if a particular article is a story about NASA. So, that's it for inferring. And in just a few minutes, you can build multiple systems for making inferences about text that previously just would have taken days or even weeks for a skilled machine learning developer. And so, I find this very exciting that both for skilled machine learning developers, as well as for people that are newer to machine learning, you can now use prompting to very quickly build and start making inferences on pretty complicated natural language processing tasks like these. In the next video, we'll continue to talk about exciting things you could do with large language models, and we'll go on to "Transforming". How can you take one piece of text and transform it into a different piece of text, such as translate it to a different language. Let's go on to the next video.
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