Decision nodes, not dictionary entries.
Each page answers when to use the concept, when to avoid it, and what to learn next.
Chunking
Chunking splits source material into retrievable pieces for search and RAG.
Embedding
Embeddings turn text or other data into vectors so similar items can be compared mathematically.
Fine-tuning
Fine-tuning adapts a model's behavior using examples, usually to improve style, format, or narrow task performance.
Hybrid Search
Hybrid search combines semantic vector retrieval with keyword or full-text search.
Long Context
Long context means putting a large amount of information directly into the model's prompt window.
RAG
Retrieval-Augmented Generation lets an LLM retrieve external knowledge before answering.
Reranking
Reranking reorders retrieved candidates so the most relevant evidence is placed first.
Vector Database
A vector database stores embeddings and retrieves nearby vectors efficiently.
Agent
An AI agent uses a model to choose steps, call tools, observe results, and continue toward a goal.
Function Calling
Function calling is a model interface pattern where the model emits structured arguments for predefined functions.
MCP
Model Context Protocol is a standard way for AI applications to connect with external tools, data, and context servers.
Tool Calling
Tool calling lets a model request external actions such as search, database lookup, calculation, or API calls.
Workflow
A workflow is a controlled sequence of steps where the application decides the path.