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, ensuring 100% data grounding and eliminating hallucinations.

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LLMs generate text based solely on patterns learned during pre-training, which frequently results in hallucinations or outdated facts.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-4o 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 to guarantee that AI support answers are strictly verified against your indexed website documentation.

Related Glossary Terms

Vector Embeddings →Zero Hallucination →Chunking →

Frequently Asked Questions

Why is RAG better than fine-tuning for customer service?

RAG allows immediate updates when policies change without retraining expensive models, guarantees verifiable source citations, and completely prevents hallucinations on company data.

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