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Plain-language AI glossary

Term 05TechniquesMeaning / context / connections

Techniques / Definition

RAG (Retrieval-Augmented Generation)

A technique that enhances AI responses by retrieving relevant information from a knowledge base before generating an answer.

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01 / Plain-language context

How RAG (Retrieval-Augmented Generation) works.

RAG solves the problem of AI hallucination and outdated knowledge by giving the model access to your specific data at query time. The process: (1) User asks a question, (2) System searches a vector database for relevant documents, (3) Retrieved documents are added to the prompt as context, (4) LLM generates an answer grounded in the retrieved data. This is how most enterprise AI chatbots work, allowing them to answer questions about company-specific documents without fine-tuning.

02 / Practical uses

Where it helps.

  1. 01Enterprise knowledge bases
  2. 02Customer support bots
  3. 03Documentation search
  4. 04Legal document analysis
03 / Keep exploring
EmbeddingsNumerical vector representations of text that capture semantic meaning, enabling similarity search and clustering.Vector DatabaseA specialized database optimized for storing and searching high-dimensional vector embeddings, enabling semantic similarity search.Context WindowThe maximum amount of text (measured in tokens) that an AI model can process in a single request, including both input and output.Fine-TuningThe process of further training a pre-trained AI model on your specific data to improve performance on domain-specific tasks.