The AI Revolution
The AI Revolution
When you use BingAI Chat or similar system to search the web or answer a question, or respond to a chat bot, your are writing a prompt to which the Large Language Model behind these systems responds. The skill of drafting a prompt to elicit a relevant, useful or intended response is called Prompt Engineering. Prompt engineering is the process of designing prompts that can effectively work with large language models like Chat GPT 4. It is an essential skill for anyone who wants to use these generative AI tools effectively.
In a sense, the art of Prompt Engineering, is the art of conversation with a highly informed, highly articulate but niave, and occassionally dishonest conversational partner. In prompt writing we are trying to both direct and focus the LLM's response while broadening its response domain sufficently to capture the "out of the box" idea, without tripping over the line of hallucination. While it is easy to imaging that LLM's are "sentient", because they often "talk" like real people, it is critical to understand that they are not, and that while they are able to hold a conversation and hold context, they do not actually have a "deep understanding" of what they are writing about, do not have an experiential background to the material, and are essentially talking from the perspective of someone who has read a lot of books but never lived a day in the sun. They can talk about the smell of roses, but have never been immersed in a bed of flowers. There is no "instinctive" linking of experience and idea - there is just the cold and mechanical illusion of the idea associated with things the machine has been told about by hearing of others' experience. The problems encountered in LLM prompting when trying to extract simple mathematical reasoning highlight this fact perfectly.
As a prompt engineer you will gain better results if you are a domain expert in the space you are working and able to sense when a response is "unlikely". You should treat the LLM like a personal assistant, rather than a project manager. One key difference between a human assistant and an LLM assistant, however is that the human will likely suggest something "out of the blue", without waiting to be prompted, while the LLM will not. This is a critical difference. While the human assistant may have a sense of ownership of the project's outcome and an interest in its success, the LLM will not. No matter how it phrases its responses, it is disconnected from the consequence of the activity with which it assists. You are solely "in charge" and solely responsible. The LLM in its assistant role couldn't care less as it is incapable of caring. When it lies to you it does not even realise it is lieing and thus "feels" no remorse or guilt. It is a machine, not a person.
The course will cover the following topics:
The course is designed for beginners and will require no prior knowledge of AI or programming. However, basic computer usage skills such as using a browser and accessing Chat GPT 4 is required.
In addition to the training and information we provide here in the following pages, if you need more information or are just interested in learning more about prompt engineering for Chat GPT 4, we recommend checking out the following resources:
These courses provide detailed information on prompt engineering and how to use it effectively with Chat GPT 4.
Prompt engineering is the process of designing prompts that can effectively work with large language models (LLMs) like Chat GPT 4. It is an essential skill for anyone who wants to use these generative AI tools effectively.
The goal of prompt engineering is to design prompts that can elicit specific responses from LLMs. These responses can be in the form of text, code, images, or other media. Effective prompt engineering 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 outputs.
The first step in prompt engineering is to understand the basics of LLMs. LLMs are designed to generate human-like text based on a given prompt. They are trained on vast amounts of text data and can generate coherent and fluent text that is often indistinguishable from human writing. LLMs can be used for a wide range of applications, including chatbots, search engines, summarization tools, and even code generation.
The next step is to learn how to write effective prompts. Effective prompts are specific, concise, and relevant to the task at hand. They should be designed to elicit the desired response from the LLM while minimizing irrelevant or problematic outputs. Effective prompts should also take into account the context in which they are used.
Advanced prompt engineering techniques involve using prompt patterns to tap into powerful capabilities within LLMs. Prompt patterns are pre-defined templates that can be used to generate specific types of outputs from LLMs. They can be used to generate text in different styles, summarize long texts into shorter ones, or even generate code snippets based on natural language prompts.
Writing prompts for specific applications requires domain-specific knowledge. For example, writing prompts for a chatbot requires an understanding of conversational language and common user queries. Writing prompts for a search engine requires an understanding of how users search for information online.
Best practices for prompt engineering involve testing and iterating on prompts to achieve the desired results. This involves experimenting with different prompts and parameters across multiple different models using a common interface. By comparing the outputs of different models and parameters, prompt engineers can iterate on prompts to achieve the desired results.
In conclusion, prompt engineering is an essential skill for anyone who wants to use LLMs effectively. It involves designing prompts that can elicit specific responses from LLMs while minimizing irrelevant or problematic outputs. Effective prompt engineering requires creativity, attention to detail, and domain-specific knowledge. By following best practices and iterating on prompts, prompt engineers can achieve the desired results with LLMs.
