Tag: RAG

10 items found

Glossary Terms

An AI agent is a software component that uses an LLM, tools, and data sources to plan steps and complete a defined goal.

AI guardrails are control layers that constrain model inputs, outputs, and tool use against safety, policy, and quality rules.

Chunking splits long text into meaningful, manageable passages that search and RAG systems can retrieve accurately.

A context window is the total token capacity a language model can read and consider while generating one response in a single request.

AI hallucination is when a model produces information that sounds plausible but is false, unsupported, or not grounded in the source.

A knowledge graph models entities such as people, products, documents, and processes with relationships that systems can query.

RAG is an AI architecture where a language model retrieves relevant passages from documents or databases before generating an answer.

Reranking re-scores an initial result set with a stronger model so the most relevant documents move to the top reliably.

Semantic search finds relevant results by comparing the meaning of queries and content, not only matching exact keywords.

A vector database stores embeddings and retrieves records by semantic similarity, making it a core layer in AI search systems.