Creating Effective Prompts with ChatGPT and GPT3: A Comparative Analysis
In today's digital landscape, AI-powered language models like ChatGPT and GPT3 have become increasingly popular for generating high-quality content. However, one of the most significant challenges facing users is effectively crafting prompts that elicit the desired response from these models. In this article, we'll delve into the world of prompting in chatGPT and GPT3, exploring the differences between zero-shot, one-shot, and few-shot prompting techniques.
Zero-Shot Prompts: Guessing Your Way to Success
Zero-shot prompting is a technique where the model is presented with no prior examples of the desired output. Instead, it relies solely on its language understanding abilities to generate a response. In this approach, the user provides an image description with adjectives and nouns related to the topic they want to explore. For instance, if we want to create an image of a female cyborg working in a Winter landscape in Norway, we would write: "a female cyborg working in a Winter landscape in Norway." The model then uses this input to generate a prompt that will produce the desired output.
The advantages of zero-shot prompting lie in its flexibility and ability to handle unexpected inputs. However, this approach also has its limitations, as the model may struggle to provide accurate results without prior examples. As we can see from our example, the generated prompt is quite good but not exactly what we wanted. This highlights the importance of refining and improving our prompts to achieve better results.
One-Shot Prompts: Building on Previous Experience
One-shot prompting takes a different approach by providing the model with a single example of the desired output. In this case, we give the model an input that includes the format we want (e.g., "I want it just the adjectives announce and at the end I want this aspect ratio") and what we're looking for in terms of content (e.g., "a female cyborg working in a Winter landscape in Norway"). By presenting a single example, the model can build upon its existing knowledge to generate a response that better meets our needs.
The benefits of one-shot prompting lie in its ability to leverage the model's past experiences and improve upon them. This approach also reduces the risk of providing inaccurate results, as the model has more context to draw from. However, it's worth noting that the effectiveness of one-shot prompts can depend on the quality and relevance of the initial example.
Few-Shot Prompts: A Hybrid Approach
Few-shot prompting combines elements of both zero-shot and one-shot techniques by providing the model with a small set of examples (typically three to five) that demonstrate the desired output. This approach allows the model to learn from a few high-quality examples while still benefiting from its language understanding abilities.
In our example, we provide three examples of what we want in terms of content: "a female cyborg working in a Winter landscape in Norway," "just the adjectives announce and at the end I want this aspect ratio," and "something similar to the previous output." By presenting these examples, the model can develop a better understanding of our preferences and generate a response that aligns with those expectations.
The advantages of few-shot prompting lie in its ability to balance flexibility and accuracy. This approach provides the model with enough context to produce high-quality results while still allowing for some nuance and variation in the output.
Conclusion
Creating effective prompts with chatGPT and GPT3 requires a deep understanding of these language models' capabilities and limitations. By exploring different prompting techniques, such as zero-shot, one-shot, and few-shot prompting, users can increase their chances of achieving successful results. Whether you're looking to generate images, text, or other types of content, mastering the art of prompting will help you get the most out of these powerful tools.
"WEBVTTKind: captionsLanguage: entoday I wanted to create a video about prompting in chat GPT and gpt3 and show the difference between zero shot one shot and few shot prompting because there are useful things you can do with these different techniques anyways let's just dive in so let's start off by looking at zero shot prompting this is where the model that in this case is shot DBT it's just guessing its best effort without having seen any examples of the results you really want beforehand so let's just head over to Chef DPT and do an example of a zero shot Pro okay so let's say my plan is that I want an uh prompt that I can use in mid-journey for an image so I just write write an image description with adjectives and nouns of a female cyborg working in Winter landscape in Norway so here the chat TP T3 or the model doesn't know anything about exactly what kind of results I want so all that is doing here is just guessing or it's a it's trying to figure out okay so this input okay so I guess I gotta give back this then it doesn't really know what I want exactly so it's just guessing but it's very good at it and as you can see here this is a very good guess of what I really wanted but it's not exactly what I wanted so I'm gonna see how we can do something about that but anyway let's just copy this uh head over to me Journey paste this prompt anyway just for fun and see what we get back and compare it with the other ones so we are back at the slide again and now let's have a look at what one shot prompting is here you can see that the model is given just one example of the result you want or what we want so if we head over to shut TPT now and give it an example of what exactly we want and see what we get back then okay so now let's give chatipity one example of uh what exactly is that we want this is the format I want the result in I want it the just the adjectives announce and at the end I want this aspect ratio that we can use in mid-journey you can see I give it an example here of what the result I want and now let's try to run that again and see what the we can get back you see now it's really trying like it's much better you see it's much more compressed than it was before it's almost perfect you see it's missing this one and but I gotta say that was very good for just a one shot example let's copy that and head over to me Journey just paste this as a chrome 2 and see what we get back and finally let's have a look at a few shot Solutions so as we can see here the fuse shot prompting is give the model a small number of examples of the results you want so what you're gonna do now is give GPT uh I guess three examples of the results we really want so here you can see now we give it you can see there's one two and yeah three examples here of what the results we really want is and let's see if something changes now it should do based on my experience so yeah you can see it starts with the same and hopefully we get this aspect ratio at the end here yeah so this was as expected but it's a very useful way to do things if you are out after a really specific output so let's just copy this head over to Mid journey and also paste this here so yeah I hope this was quite as you can see here like we started with a zero shot here now the model didn't know anything about what we really wanted it just guessed them it gave it a small hint here with uh one shot and finally we try to reel it spoon feed it like this is what we want we want this kind of output in this format and it gave us that so hopefully you understood that finally let's compare the three images from mid Journey okay so here are the images we got back from me Journey uh there's nothing really much to say here I think they all look good and that's giving a lot of credit to Mid Journey because remember other one on the left the zero shot one was just a wall of text and it came out pretty good if you compare it to the one on the on the few short one which I think was my favorite but it's not blood separate from the zero shot so I think midi Journey did a very good jobtoday I wanted to create a video about prompting in chat GPT and gpt3 and show the difference between zero shot one shot and few shot prompting because there are useful things you can do with these different techniques anyways let's just dive in so let's start off by looking at zero shot prompting this is where the model that in this case is shot DBT it's just guessing its best effort without having seen any examples of the results you really want beforehand so let's just head over to Chef DPT and do an example of a zero shot Pro okay so let's say my plan is that I want an uh prompt that I can use in mid-journey for an image so I just write write an image description with adjectives and nouns of a female cyborg working in Winter landscape in Norway so here the chat TP T3 or the model doesn't know anything about exactly what kind of results I want so all that is doing here is just guessing or it's a it's trying to figure out okay so this input okay so I guess I gotta give back this then it doesn't really know what I want exactly so it's just guessing but it's very good at it and as you can see here this is a very good guess of what I really wanted but it's not exactly what I wanted so I'm gonna see how we can do something about that but anyway let's just copy this uh head over to me Journey paste this prompt anyway just for fun and see what we get back and compare it with the other ones so we are back at the slide again and now let's have a look at what one shot prompting is here you can see that the model is given just one example of the result you want or what we want so if we head over to shut TPT now and give it an example of what exactly we want and see what we get back then okay so now let's give chatipity one example of uh what exactly is that we want this is the format I want the result in I want it the just the adjectives announce and at the end I want this aspect ratio that we can use in mid-journey you can see I give it an example here of what the result I want and now let's try to run that again and see what the we can get back you see now it's really trying like it's much better you see it's much more compressed than it was before it's almost perfect you see it's missing this one and but I gotta say that was very good for just a one shot example let's copy that and head over to me Journey just paste this as a chrome 2 and see what we get back and finally let's have a look at a few shot Solutions so as we can see here the fuse shot prompting is give the model a small number of examples of the results you want so what you're gonna do now is give GPT uh I guess three examples of the results we really want so here you can see now we give it you can see there's one two and yeah three examples here of what the results we really want is and let's see if something changes now it should do based on my experience so yeah you can see it starts with the same and hopefully we get this aspect ratio at the end here yeah so this was as expected but it's a very useful way to do things if you are out after a really specific output so let's just copy this head over to Mid journey and also paste this here so yeah I hope this was quite as you can see here like we started with a zero shot here now the model didn't know anything about what we really wanted it just guessed them it gave it a small hint here with uh one shot and finally we try to reel it spoon feed it like this is what we want we want this kind of output in this format and it gave us that so hopefully you understood that finally let's compare the three images from mid Journey okay so here are the images we got back from me Journey uh there's nothing really much to say here I think they all look good and that's giving a lot of credit to Mid Journey because remember other one on the left the zero shot one was just a wall of text and it came out pretty good if you compare it to the one on the on the few short one which I think was my favorite but it's not blood separate from the zero shot so I think midi Journey did a very good job\n"