AI Glossarytechniques

RAG (Retrieval-Augmented Generation)

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

How It 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RAG solves the problem of AI hallucination and outdated knowledge by giving the model access to your specific data at query time.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.

Common Use Cases

  • 1Enterprise knowledge bases
  • 2Customer support bots
  • 3Documentation search
  • 4Legal document analysis

Related Terms

Context Window

The maximum amount of text (measured in tokens) that an AI model can process in a single request, including both input and output.

Embeddings

Numerical vector representations of text that capture semantic meaning, enabling similarity search and clustering.

Fine-Tuning

The process of further training a pre-trained AI model on your specific data to improve performance on domain-specific tasks.

Vector Database

A specialized database optimized for storing and searching high-dimensional vector embeddings, enabling semantic similarity search.

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