What is prompt engineering?
Prompt engineering is a relatively new discipline for developing and optimizing prompts to efficiently use language models (LMs) for a wide variety of applications and research topics. Prompt engineering skills help to better understand the capabilities and limitations of large language models (LLMs).
Researchers use prompt engineering to improve the capacity of LLMs on a wide range of common and complex tasks such as question answering and arithmetic reasoning. Developers use prompt engineering to design robust and effective prompting techniques that interface with LLMs and other tools.
Prompt engineering is not just about designing and developing prompts. It encompasses a wide range of skills and techniques that are useful for interacting and developing with LLMs. It's an important skill to interface, build with, and understand capabilities of LLMs. You can use prompt engineering to improve safety of LLMs and build new capabilities like augmenting LLMs with domain knowledge and external tools.
Motivated by the high interest in developing with LLMs, we have created this new prompt engineering guide that contains all the latest papers, advanced prompting techniques, learning guides, model-specific prompting guides, lectures, references, new LLM capabilities, and tools related to prompt engineering.
What are some prompt engineering use cases?
Prompt engineering techniques are used in sophisticated AI systems to improve user experience with the learning language model. Here are some examples.
Subject matter expertise
Prompt engineering plays a key role in applications that require the AI to respond with subject matter expertise. A prompt engineer with experience in the field can guide the AI to reference the correct sources and frame the answer appropriately based on the question asked.
For example, in the medical field, a physician could use a prompt-engineered language model to generate differential diagnoses for a complex case. The medical professional only needs to enter the symptoms and patient details. The application uses engineered prompts to guide the AI first to list possible diseases associated with the entered symptoms. Then it narrows down the list based on additional patient information.
Critical thinking
Critical thinking applications require the language model to solve complex problems. To do so, the model analyzes information from different angles, evaluates its credibility, and makes reasoned decisions. Prompt engineering enhances a model's data analysis capabilities.
For instance, in decision-making scenarios, you could prompt a model to list all possible options, evaluate each option, and recommend the best solution.
Creativity
Creativity involves generating new ideas, concepts, or solutions. Prompt engineering can be used to enhance a model's creative abilities in various scenarios.
For instance, in writing scenarios, a writer could use a prompt-engineered model to help generate ideas for a story. The writer may prompt the model to list possible characters, settings, and plot points then develop a story with those elements. Or a graphic designer could prompt the model to generate a list of color palettes that evoke a certain emotion then create a design using that palette.
