4/8/2023 0 Comments Wolframalpha turkey formulabombs out! (well, it is a preview/beta version, after all ). Of course, it wouldn't make much sense to type all this out just to get a single answer, but with ChatGPT's conversational interface, you can continue to ask it questions until it, err. Providing these examples as context will help ChatGPT correctly classify the new movie description, " A group of astronauts on a mission to save humanity from extinction by colonizing a new planet.", as " Sci-Fi/Adventure" genre. For learning where there is only one example, you might also hear it being called " one-shot" learning.įew-shot learning in OpenAI models can be implemented at both the ChatGPT prompt, as well as programmatically by calling the OpenAI API (Application Programming Interface) " completion" endpoint.įor example, using prompt-based few-shot learning in ChatGPT to classify movie genres, you can simply add a selection of examples to your prompt, as follows: Example of "few-shot learning" to help ChatGPT classify film genres. The oddly named "few-shot" learning, is where the model is trained to recognise and classify a new object or concept with a small number of training examples, typically less than 10, but numbers can vary. These techniques are known as " few-shot learning" and " fine-tuning". In the language models developed by OpenAI, there are two primary techniques used to activate its vast store of knowledge and improve the accuracy of responses to prompts. In fact, the more information you provide, the better that ChatGPT and other LLM models can understand what you're asking and can provide a more accurate response in return. While ChatGPT is based on a natural language generation model and doesn't require specific prompts, it does perform much better when you provide more context and specific examples. Prompt engineering, on the other hand, is a more dynamic process that involves the model learning on a prompt-by-prompt basis sometimes (known as " few-shot learning", more later), or through creating a fine-tuned model with specialised learning " prompt-completion pairs" uploaded from a file (again, more later). Not what Prompt Engineers do every day, but fun to ask nonetheless. Whilst most AI companies have teams responsible for testing the trained model and implementing safety measures to reduce bias and hateful outcomes, this process typically occurs before the AI model is made available to the general public. It's tempting to imagine that the work of a Prompt Engineer involves spending all day long at a computer shooting riddles at ChatGPT to see if it can solve them, and then adjusting the model to get the best results. What are some techniques to use in Prompt Engineering? Job for "Prompt Engineer and Librarian" as advertised at Anthropic, an AI safety company. The role is now so in demand, that AI companies, flush with cash, are paying hundreds of thousands of dollars to people with the right skills, or aptitude. This role, typically recruited from people with a " hacker mindset", consists of constructing carefully phrased prompts that explain to the AI model exactly what they want and how they want it. Typing your question or query into an LLM like ChatGPT is also known as " prompting", and simply by changing the way you phrase your prompt, can give you very different results.īecause of the variability of results from LLMs, like ChatGPT, the AI industry has recently come up with a new specialist role, the " Prompt Engineer", whose job it is to extract the maximum value from AI models. One of the problems is that LLMs have got so damned complicated, and the " black box" neural nets through which they derive their answers are so opaque, that users are still figuring out how to get the best results from them. You might have been given incorrect, or in some instances, made-up information, as I've discussed at some length in previous newsletters, or even stumbled across some mysterious " unspeakable tokens" that ChatGPT is unable to reproduce. If you've ever interacted with ChatGPT or any other Large Language Model (LLM), you may have encountered instances where the response was not exactly what you expected, or, indeed, up to scratch. This article will give you a solid grounding and signpost you to further resources. Whether you're a power user, researcher, or entrepreneur setting up a new AI business, "Prompt Engineering", is a topic you'll need to master to get the best results. Hi folks, in this week's newsletter, I'm going to discuss the relatively new topic of "prompt engineering", what it is and some techniques and "magic phrases" you can use to get better results from Large Language Models (LLMs) like ChatGPT.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |