Prompt engineering

Category: AI

AI Prompt

write an explanation for Prompt engineering

Salesforce - Vendor Definition

Prompt engineering means figuring out how to ask a question to get exactly the answer you need. It’s carefully crafting or choosing the input (prompt) that you give to a machine learning model to get the best possible output.

Apple Intelligence

Prompt engineering is a technique used to improve the performance of large language models (LLMs) like GPT-3. It involves designing prompts that guide the LLM to generate more relevant and informative responses. These prompts can be as simple as providing additional context or examples, or as complex as using advanced natural language processing (NLP) techniques. By carefully crafting prompts, prompt engineers can significantly enhance the quality of the LLM's output.

Anthropic Claude

#NAME?

Prompt engineering is like having a conversation with a powerful but somewhat literal-minded AI assistant. It's the art and science of crafting effective instructions (prompts) to guide a large language model (LLM) toward generating the desired output.

Think of it like this: imagine you have a genie in a bottle that can grant wishes, but it takes your wishes very literally. You need to be precise and specific with your wording to get the desired outcome. Prompt engineering is about learning how to communicate effectively with the LLM "genie" to get the best results.

Why is prompt engineering important?

Improved output quality: A well-crafted prompt can significantly improve the quality, relevance, and accuracy of the LLM's output.
Unlocking hidden capabilities: By experimenting with different prompts, you can discover and unlock hidden capabilities of the LLM, getting it to perform tasks it wasn't explicitly trained for.
Tailoring to specific tasks: Prompt engineering allows you to tailor the LLM's behavior to specific tasks and domains, making it more useful for various applications.
Controlling output format: You can use prompts to specify the desired format of the output, such as a list, a paragraph, or a code snippet.
Key techniques in prompt engineering:

Clear and specific instructions: Be clear and concise in your instructions, avoiding ambiguity or vagueness.
Contextual information: Provide relevant context and background information to help the LLM understand the task.
Examples: Include examples of the desired output to guide the LLM's response.
Constraints: Specify constraints or limitations on the output, such as length, format, or style.
Iterative refinement: Experiment with different prompts and refine them based on the LLM's responses.
Examples of prompt engineering:

Instead of: "Write a poem about nature."
Try: "Write a short poem about the beauty of a sunset over the ocean, using vivid imagery and metaphors."
Instead of: "Translate this text."
Try: "Translate this text from English to Spanish, paying attention to preserving the original tone and meaning."
Instead of: "Summarize this article."
Try: "Summarize this article in three bullet points, focusing on the key findings and implications."
Who uses prompt engineering?

AI researchers: To explore the capabilities and limitations of LLMs.
Developers: To build AI applications that leverage LLMs for various tasks.
Content creators: To generate creative content, such as stories, poems, and scripts.
Data scientists: To analyze and interpret data using LLMs.
Prompt engineering is a rapidly evolving field, with new techniques and best practices emerging constantly. As LLMs become more powerful and versatile, prompt engineering will play an increasingly important role in harnessing their full potential.

#NAME?