Prompt Engineering
The practice of crafting effective inputs to get desired outputs from AI models.
In-depth explanation
Prompt engineering designs inputs that guide LLMs to produce accurate, relevant outputs. Techniques include few-shot learning (providing examples), chain-of-thought (encouraging step-by-step reasoning), and role prompting (assigning personas). Good prompts are clear, specific, and provide appropriate context. It's becoming a key skill for working with AI.
Examples
More in Generative AI
GPT
Generative Pre-trained Transformer, a family of large language models trained to generate text.
Large Language Model (LLM)
AI models trained on vast text data that can generate and understand human-like text.
Generative Adversarial Network (GAN)
Two neural networks competing against each other to generate realistic synthetic data.
Diffusion Model
Generative models that learn to create data by reversing a gradual noising process.
RAG (Retrieval-Augmented Generation)
Combining retrieval systems with language models to generate responses grounded in external knowledge.
Hallucination
When AI models generate plausible-sounding but factually incorrect or fabricated information.
Master Prompt Engineering.
Learn how to apply this concept with hands-on projects in our comprehensive AI programs.