🤖 The Art of Prompt Engineering: A Guide to Communicating with Large Language Models
As the demand for prompt engineers continues to rise in the workforce and open-source community, it's important to understand the core concepts and steps involved in prompt engineering. In this article, we'll cover some of the most widely used prompt engineering tactics, including chain of thought prompting, generated knowledge prompting, least to most prompting, and self-refined prompting. We'll also explore how directional stimulus prompting can guide large language models towards a desired output through user-provided cues.
🧠 Chain of Thought Prompting
Chain of thought prompting utilizes large language models' problem-solving abilities to logically think its way through a task step by step. For example, using chat GPT as an example, we can introduce a simple arithmetic problem: "A store had 45 oranges. A customer came in and bought some. The store now has 30 oranges. How many oranges did the customer purchase?" By telling the large language model to think step by step, it will give us the entire chain of thought instead of simply saying 15 oranges.
Chain of thought prompting is not just for arithmetic and math problems. You can also use this to break down and describe the functions or the step-by-step process of any issue you're facing.
📚 Generated Knowledge Prompting
Generated knowledge prompting cues the model for factual, relevant information to your input and can be highly efficient in private LLMs that are trained for a specific purpose. For example, a private law firm may utilize generated knowledge to locate all relevant case information through an LLM familiar with their personal data.
Generated knowledge prompt is a wonderful opener when communicating with an LLM, as this sets the grounds for what you're going to be talking about. You can then go in and precisely ask targeted questions about any of the bullet points or even ask for a more brief or larger and more expansive summary.
🔍 Least to Most Prompting
Least to most prompting breaks down a larger problem into sub-problems, solving each sequentially. Least to most prompting can be used if specific sub-problem information is deemed relevant for a reference later on.
Using chat GPT, let's go ahead and input our question: "How do I lose weight?" and then specify that we want it to break down the sub-problems. You can see once again we receive a quite expansive response instead of just putting the in bullet points. It gives us full explanation and a breakdown of each bullet point uniquely.
🎨 Self-Refined Prompting
Self-refined prompting prompts the model to not only solve a problem but consider and implement its solution as well. For example, if we wanted to increase the readability of a line of code or a string of code, we can input it into chat GPT and ask it to increase the readability. It will actually spit out a box of code that we can easily copy from right here. The beauty of self-refined prompts is we can do this endlessly. If we go back into the chat window and say, "Please implement the changes," it will go ahead and spit that right back out at us. We can then take this and have it modified even further any way we want almost instantly with a large language model.
📝 Directional Stimulus Prompting
Directional stimulus prompting guides the LLM towards a desired output through user-provided cues. For example, if we're a content creator that needs a blog article, we can input our desired topic and keywords into chat GPT and have it generate an article for us. This can be wildly helpful for content creators, marketing managers, and business owners alike.
While these techniques are simple to understand, they're only the tip of the iceberg. As things such as text-to-image and text-to-video tools are constantly evolving, as well as the base of LLMs that they're incorporated on, the possibilities are endless.
🌟 Highlights
- Prompt engineering is the ability to communicate effectively with a large language model to reach a specific desired outcome with the understanding of a few key concepts.
- Chain of thought prompting utilizes LLMs' problem-solving abilities to logically think its way through a task step by step.
- Generated knowledge prompting cues the model for factual, relevant information to your input and can be highly efficient in private LLMs that are trained for a specific purpose.
- Least to most prompting breaks down a larger problem into sub-problems, solving each sequentially.
- Self-refined prompting prompts the model to not only solve a problem but consider and implement its solution as well.
- Directional stimulus prompting guides the LLM towards a desired output through user-provided cues.
❓ FAQ
Q: What is prompt engineering?
A: Prompt engineering is the ability to communicate effectively with a large language model to reach a specific desired outcome with the understanding of a few key concepts.
Q: What is chain of thought prompting?
A: Chain of thought prompting utilizes LLMs' problem-solving abilities to logically think its way through a task step by step.
Q: What is generated knowledge prompting?
A: Generated knowledge prompting cues the model for factual, relevant information to your input and can be highly efficient in private LLMs that are trained for a specific purpose.
Q: What is least to most prompting?
A: Least to most prompting breaks down a larger problem into sub-problems, solving each sequentially.
Q: What is self-refined prompting?
A: Self-refined prompting prompts the model to not only solve a problem but consider and implement its solution as well.
Q: What is directional stimulus prompting?
A: Directional stimulus prompting guides the LLM towards a desired output through user-provided cues.
Resources:
- https://www.voc.ai/product/ai-chatbot (AI chatbot product that can automatically reduce large amounts of work on customer services)
