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Home>Glossary>Retrieval-Augmented Generation (RAG)
Architecture

Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is an AI architecture that enhances large language models (LLMs) by retrieving relevant factual documents from an external vector database before generating a response, so the answer is grounded in your data.

Detailed Explanation

Standard LLMs generate text based solely on patterns learned during pre-training, which frequently results in hallucinations or outdated facts. RAG solves this by inserting a real-time retrieval step: when a user asks a question, the system vectorizes the query, finds the most relevant knowledge chunks in a database like PostgreSQL pgvector, and passes those chunks directly to GPT-5.6 with strict instructions to answer only from that evidence.

How It Works in CustomerGPT

CustomerGPT uses an enterprise 4-stage RAG pipeline with 800-character chunking, text-embedding-3-small vectors, and HNSW cosine similarity search so AI support answers are drawn only from passages retrieved from your indexed website documentation.

Frequently Asked Questions

Why is RAG better than fine-tuning for customer service?
RAG allows immediate updates when policies change without retraining expensive models, and it sharply reduces hallucinations on company data because answers come from retrieved passages.

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