RAG Architecture
Retrieval-Augmented Generation (RAG) connects Large Language Models to private, proprietary enterprise databases to deliver factual, zero-hallucination answers with source citations.
Why RAG is Essential for Enterprise AI
Base Large Language Models are static snapshots of public internet data. RAG eliminates hallucinations by fetching relevant internal documents from a vector store at query time, injecting the exact factual context directly into the prompt.
The Complete RAG Data Pipeline
- check_circle Document Ingestion & Chunking: Breaking PDFs, DOCX, and web pages into semantic text chunks with metadata.
- check_circle Vector Embedding: Converting text chunks into high-dimensional floating-point vectors.
- check_circle Vector Storage & Indexing: Indexing embeddings in high-speed databases (Pinecone, Qdrant, pgvector).
- check_circle Semantic Query & Hybrid Retrieval: Combining Keyword BM25 search with Vector Cosine Similarity.
- check_circle Re-Ranking & Generation: Re-ordering retrieved chunks using a Cross-Encoder before sending context to the LLM.
help Frequently Asked Questions
Q: What is the difference between Naive RAG and Advanced RAG?
A: Naive RAG relies solely on simple vector similarity search. Advanced RAG incorporates hybrid search, contextual re-ranking, and parent document retrieval.
Q: How do you measure RAG accuracy?
A: We use evaluation frameworks like Ragas to measure Faithfulness, Answer Relevance, Context Precision, and Context Recall.
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