Practical Applications
18 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.
Edge AI
Edge AI runs AI models directly on local devices instead of the cloud, enabling privacy, low latency, and offline functionality through quantized and distilled models.
Embodied AI
AI systems designed to perceive and interact with physical or virtual environments, bridging the gap between digital reasoning and real-world action.
MLOps
MLOps applies DevOps practices to machine learning: automating deployment, monitoring, and maintenance of ML models in production.
Semantic Search
Semantic search retrieves information based on meaning rather than keywords, using AI embeddings and vector similarity to find relevant results.
Structured Output
Structured output forces LLMs to produce machine-readable data (like JSON) matching a predefined schema, making AI outputs reliably parseable by applications.
Knowledge Graph
A knowledge graph stores real-world entities and their relationships as a structured network, enabling machines to reason over connected facts and enhance AI accuracy.
System Prompt
A system prompt is the developer's instruction set that defines an LLM's behavior, role, constraints, and output format for a specific application.
AI API
An AI API is a web service that lets developers integrate AI model capabilities into applications via simple HTTP requests, without running models themselves.
Semantic Training Gap
The gap between an AI model's statistical language fluency and its grounded understanding of domain-specific operational semantics, leading to hallucinated identifiers and cascading failures in industrial applications.