Practical Applications
7 concepts
Chain-of-Thought Prompting
A prompting technique that asks LLMs to reason step-by-step before answering, dramatically improving accuracy
Few-Shot Prompting
Providing a few worked examples in the prompt to guide an LLM's behavior — typically improving accuracy by 20-30% over zero-shot
Generative Engine Optimization (GEO)
Optimizing content for AI discovery instead of just search engines — answer-first structure, structured data, and question-oriented titles.
Grounding in AI
Anchoring LLM responses to verified external sources to reduce hallucinations and enable citation
In-Context Learning (ICL)
The ability of LLMs to learn new tasks from examples provided in the prompt — without any weight updates or fine-tuning
Prompt Engineering
The systematic practice of designing effective prompts to get optimal results from LLMs
Zero-Shot Prompting
Asking an LLM to perform a task using only instructions and no examples — the fastest and cheapest prompting approach