Building Enterprise RAG Pipelines: From POC to Production

The RAG Revolution

Retrieval-Augmented Generation has emerged as the most practical way to give large language models access to proprietary enterprise data without expensive fine-tuning.

Architecture Overview

Ingestion Pipeline

1. Document Processing: Extract text from PDFs, DOCX, and web pages
2. Chunking Strategy: Use semantic chunking for better retrieval
3. Embedding Generation: Generate vector embeddings using models like text-embedding-3-large
4. Vector Storage: Store in a vector database (Pinecone, Weaviate, or pgvector)

Retrieval Pipeline

1. Query Understanding: Classify intent and reformulate queries
2. Hybrid Search: Combine dense vector search with sparse BM25
3. Re-ranking: Use a cross-encoder to re-rank retrieved chunks

Generation Pipeline

1. Context Assembly: Stitch chunks into a coherent prompt
2. LLM Inference: Route to GPT-4, Claude, or on-premise models
3. Citation & Grounding: Ensure every claim links to a source

Production Considerations

• Monitoring: Track retrieval precision, answer quality, and latency
• Security: Implement document-level access controls
• Cost: Use caching and tiered models to manage token spend

Results

Enterprises deploying production RAG report 40-60% reduction in knowledge worker search time.