Definition
Prompt engineering is the practice of designing and optimizing input prompts to guide the behavior of large language models.
Purpose
The purpose is to improve output quality, reliability, and alignment with user goals. It is widely used in applications of generative AI.
Importance
- Critical for effective use of LLMs.
- Helps reduce hallucinations and biases.
- Requires experimentation and domain expertise.
- Rapidly evolving with tools like prompt chaining and templates.
How It Works
- Define the task and desired output.
- Design clear and specific prompts.
- Test prompts with the LLM.
- Refine based on results.
- Apply techniques like few-shot or chain-of-thought prompting.
Examples (Real World)
- ChatGPT: optimized prompts for summarization or Q&A.
- MidJourney: prompts guide AI art generation.
- Google Bard: prompt optimization for factual accuracy.
References / Further Reading
- Reynolds & McDonell. “Prompt Programming for Large Language Models.” arXiv.
- OpenAI Cookbook: Prompt Engineering Guide.
- Stanford HAI Research on Prompt Design.