The AI knowledge
glossary
Evaluating AI assistants means wading through jargon. Here is every concept that matters — RAG, embeddings, hybrid search, evals, MCP — explained plainly, with how each one works in practice.
Retrieval & Search
Embeddings
Numeric vector representations of text in which semantically similar passages end up close together, enabling search by meaning rather than exact words.
Hybrid Search
A retrieval strategy that combines semantic (vector) search with keyword (full-text) search and merges the results, capturing both meaning and exact matches.
Semantic Search
Search that ranks results by meaning similarity between the query and documents, using embeddings, rather than by matching keywords.
Vector Database
A data store optimized for saving embedding vectors and finding the nearest neighbors of a query vector quickly, at scale.
Chunking
Splitting documents into smaller passages before embedding, so retrieval can return the precise section that answers a question rather than a whole page.
Generation & Quality
RAG (Retrieval-Augmented Generation)
An AI architecture that retrieves relevant documents from a trusted knowledge source and passes them to a language model, so answers are grounded in real content instead of the model's training data.
Hallucination
When a language model generates fluent, confident content that is factually wrong or entirely invented.
Citations
Links from an AI answer back to the specific source documents used to generate it, allowing users to verify every claim.
Prompt Injection
An attack in which adversarial instructions are placed in user input or in retrieved content to manipulate an AI system into ignoring its rules or leaking data.
Eval (LLM Evaluation)
A systematic method for measuring the quality of AI-generated answers — often using a second model as a judge — so quality is tracked rather than assumed.
Protocols & Infrastructure
Knowledge Base
A curated, indexed collection of an organization's knowledge — docs, wikis, tickets, repos — that an AI assistant retrieves from when answering questions.
MCP (Model Context Protocol)
An open protocol that lets AI applications (like IDE assistants) connect to external tools and data sources through a standard JSON-RPC interface.
A2A (Agent-to-Agent)
A protocol for AI agents to discover and communicate with each other, exchanging messages through published capability descriptions called AgentCards.
LLM Gateway
An abstraction layer between an application and language-model providers, allowing models to be swapped, routed, and managed without changing application code.
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