Prompt engineering is both art and science - crafting inputs that guide LLMs to produce accurate, relevant responses. Key techniques include: zero-shot (direct questions), few-shot (providing examples), chain-of-thought (asking for reasoning steps), and system prompts (setting context and behavior). Advanced patterns include ReAct (Reasoning + Acting), tree-of-thought, and meta-prompting. Effective prompts are specific, provide context, specify output format, and include constraints. As models improve, prompt engineering evolves - what works for GPT-3 may differ from Claude or GPT-4. It's a critical skill for building AI applications.
🧠 AI & LLMs beginner
Prompt Engineering
The practice of designing and optimizing inputs to AI models to achieve desired outputs.
</> Related Terms
LLM (Large Language Model)
AI models trained on massive text datasets to understand and generate human-like text.
Context Window
The maximum amount of text (measured in tokens) that an LLM can process in a single interaction.
Fine-tuning
Adapting a pre-trained model to specific tasks or domains by training on specialized data.
Hallucination
When AI models generate plausible-sounding but factually incorrect or fabricated information.