What is RAG (Retrieval Augmented Generation)?

Retrieval Augmented Generation (RAG) is an AI architecture that enhances large language models by retrieving relevant information from external sources before generating responses. This grounds AI outputs in factual, current data rather than relying solely on training data.

How RAG Works

1. Query understanding: AI parses the user question
2. Retrieval: System searches knowledge bases for relevant content
3. Augmentation: Retrieved content is added to the prompt
4. Generation: LLM generates response using retrieved context

Why RAG Matters

RAG solves critical LLM limitations:

• Prevents hallucinations: Responses grounded in actual documents
• Current information: Uses live data, not training cutoff
• Domain accuracy: Leverages your specific knowledge
• Citability: Can reference source documents

RAG vs Fine-Tuning

• Fine-tuning: Retrains model on your data (expensive, static)
• RAG: Retrieves your data at runtime (flexible, current)

Most enterprise deployments use RAG because content changes frequently and fine-tuning is resource-intensive.

RAG Components

• Vector database: Stores embeddings of your content
• Embedding model: Converts text to searchable vectors
• Retriever: Finds relevant documents for each query
• LLM: Generates responses from retrieved context

RAG Quality Factors

• Chunk size: How content is segmented
• Retrieval accuracy: Finding the right documents
• Context window: How much retrieved content LLM can use
• Prompt engineering: How retrieved content is presented

Maven AGI Difference: Our Knowledge Graph goes beyond basic RAG. We ingest and understand relationships across all your content sources, delivering precise answers with citations. This architecture enables the 90%+ resolution rates our customers achieve.

Book a demo to see advanced RAG in action.