Prompt engineering

See also: The [ jargon] prompt engineering using multiple NLP datasets showed good performance on new tasks. A description for handling prompts reported that over 2,000 public prompts for around 170 datasets were available in February 2022. Textual prompting [ ] Chain-of-thought [ ] Chain-of-thought prompting (CoT) improves the reasoning ability of large language models (LLMs) by prompting them to generate a series of intermediate steps that lead to the final answer of a multi-step problem. LLMs that are trained on large amounts of text using before giving the final answer to a multi-step problem. For example, given the question “Q: The cafeteria had 23 apples. If they used 20 to make lunch and bought 6 more, how many apples do they have?”, a CoT prompt might induce the LLM to answer with steps of reasoning that mimic a Chain-of-thought prompting improves the performance of LLMs on average on both arithmetic and commonsense tasks in comparison to standard prompting methods. CoT prompting is an Method [ ] There are two main methods to elicit chain-of-thought reasoning: Variants [ ] Generated knowledge prompting first prompts the model to generate relevant facts for completing the prompt, then proceed to complete the prompt. The completion quality is usually higher, as the model can be conditioned on relevant facts. Self-consistency decoding Tree-of-thought prompting generalizes CoT by prompting the model to generate one or more "possible next steps", and then running the mo...

Screenshot of the course main view The realm of artificial intelligence has been enriched by the recent collaboration between OpenAI and the learning platform DeepLearning.AI in the form of a comprehensive course on Prompt Engineering. This course — currently available for free — opens a new window into enhancing our interactions with artificial intelligence models like ChatGPT. So, how do we fully leverage this learning opportunity? ⚠️ All examples provided though this article are from the course. Let’s discover it all together! 👇🏻 Prompt Engineering centers around the science and art of formulating effective prompts to generate more precise outputs from AI models. Put it simply, how to get better output from any AI model. As AI agents have become our new default, it is of utter importance to understand how to take the most advantage of it. This is why OpenAI together with DeepLearning.AI have designed a course to better understand how to craft good prompts. Although the course primarily targets developers, it also provides value to non-tech users by offering techniques that can be applied via a simple web interface. So either way, just stay with me! Today’s article will talk about the first module of this course: How to effectively get a desired output from ChatGPT. Understanding how to maximize ChatGPT’s output requires familiarity with two key principles: clarity and patience. Easy right? Let’s break them down! :D Principle I: The clearer the better The first principle...

In this article This guide will walk you through some advanced techniques in prompt design and prompt engineering. If you're new to prompt engineering, we recommend starting with our While the principles of prompt engineering can be generalized across many different model types, certain models expect a specialized prompt structure. For Azure OpenAI GPT models, there are currently two distinct APIs where prompt engineering comes into play: • Chat Completion API. • Completion API. Each API requires input data to be formatted differently, which in turn impacts overall prompt design. The Chat Completion API supports the ChatGPT and GPT-4 models. These models are designed to take input formatted in a The Completion API supports the older GPT-3 models and has much more flexible input requirements in that it takes a string of text with no specific format rules. Technically the ChatGPT models can be used with either APIs, but we strongly recommend using the Chat Completion API for these models. To learn more, please consult our The techniques in this guide will teach you strategies for increasing the accuracy and grounding of responses you generate with a Large Language Model (LLM). It is, however, important to remember that even when using prompt engineering effectively you still need to validate the responses the models generate. Just because a carefully crafted prompt worked well for a particular scenario doesn't necessarily mean it will generalize more broadly to certain use c...

In this article Prompts play a crucial role in communicating and directing the behavior of Large Language Models (LLMs) AI. They serve as inputs or queries that users can provide to elicit specific responses from a model. The subtleties of prompting Effective prompt design is essential to achieving desired outcomes with LLM AI models. Prompt engineering, also known as prompt design, is an emerging field that requires creativity and attention to detail. It involves selecting the right words, phrases, symbols, and formats that guide the model in generating high-quality and relevant texts. If you've already experimented with ChatGPT, you can see how the model's behavior changes dramatically based on the inputs you provide. For example, the following prompts produce very different outputs: Please give me the history of humans. Please give me the history of humans in 3 sentences. The first prompt produces a long report, while the second prompt produces a concise response. If you were building a UI with limited space, the second prompt would be more suitable for your needs. Further refined behavior can be achieved by adding even more details to the prompt, but its possible to go too far and produce irrelevant outputs. As a prompt engineer, you must find the right balance between specificity and relevance. When you work directly with LLM models, you can also use other controls to influence the model's behavior. For example, you can use the temperature parameter to control the ran...