Practical Applications
10 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.
GraphRAG
A RAG architecture that pre-builds a knowledge graph from documents, enabling multi-hop reasoning over entity relationships instead of flat vector search.
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
AI Robotics
The integration of advanced AI foundation models with robotic hardware to create machines capable of autonomous, real-world reasoning and physical manipulation.
Embodied AI
AI systems designed to perceive and interact with physical or virtual environments, bridging the gap between digital reasoning and real-world action.