AI GlossaryPrompt EngineeringENTRY — Generative AI
[Generative AI]
Prompt Engineering.
The practice of crafting effective inputs to get desired outputs from AI models.
In-depth explanation
01Prompt 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
02EX. 01
System prompts for ChatGPT
EX. 02
Few-shot examples for classification
More in Generative AI
0301Diffusion ModelGenerative models that learn to create data by reversing a gradual noising process.02Generative Adversarial Network (GAN)Two neural networks competing against each other to generate realistic synthetic data.03GPTGenerative Pre-trained Transformer, a family of large language models trained to generate text.04HallucinationWhen AI models generate plausible-sounding but factually incorrect or fabricated information.05Large Language Model (LLM)AI models trained on vast text data that can generate and understand human-like text.06RAG (Retrieval-Augmented Generation)Combining retrieval systems with language models to generate responses grounded in external knowledge.
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