Large language models are powerful but hallucinate when asked about topics outside their training data — including your company's proprietary information. Retrieval-Augmented Generation (RAG) solves this by connecting AI to your knowledge base, enabling it to answer questions with citations from your actual documents, databases, and internal wikis.
Understanding RAG Architecture
RAG operates in two phases: retrieval and generation. First, the user's query is converted to a vector embedding. This embedding is used to search a vector database containing your indexed documents. The most relevant document chunks are retrieved and injected into the LLM's context alongside the user's question. The LLM then generates an answer grounded in your actual data — dramatically reducing hallucination.
Embedding Models & Chunking Strategy
Embeddings are numerical representations of text that capture semantic meaning. We use OpenAI's text-embedding-3-large or open-source alternatives like BGE for privacy-sensitive deployments. Chunking — how you split documents — is critical. Too large and you waste context; too small and you lose meaning. Our approach: 512-token chunks with 50-token overlap, respecting paragraph and section boundaries.
Choosing the Right Vector Database
Pinecone: Best for managed, serverless deployments with zero ops overhead. Weaviate: Best for hybrid search combining vector and keyword matching. Qdrant: Best for self-hosted, high-performance requirements. ChromaDB: Best for prototyping and small-scale deployments. For enterprise clients, we typically deploy Weaviate for its hybrid search capabilities and flexible filtering.
Hybrid Search: Beyond Pure Vector Similarity
Pure vector search misses exact-match queries (product codes, names, dates). Hybrid search combines vector similarity with traditional keyword matching using BM25. We implement reciprocal rank fusion to merge results from both retrieval methods, consistently improving relevance by 20-30% over pure vector search.
Evaluating RAG Quality
We measure RAG systems on four dimensions: 1) Faithfulness — does the answer accurately reflect the retrieved documents? 2) Relevance — are the retrieved documents actually relevant to the query? 3) Completeness — does the answer address all aspects of the question? 4) Citation accuracy — can every claim be traced to a source? We use RAGAS framework for automated evaluation.
Production RAG Pipeline
Our production pipeline includes: document ingestion with automatic metadata extraction, multi-stage retrieval (semantic search → re-ranking → MMR diversity), prompt engineering with citation formatting, response caching for common queries, and feedback loops that improve retrieval quality over time. This architecture serves 50,000+ queries daily for our enterprise clients.
RAG Proof-of-Concept
NeoKlyn builds RAG proof-of-concepts in 2 weeks using your actual data. See how AI can answer questions about your business with cited, accurate responses before committing to a full deployment.
Conclusion
RAG transforms AI from a general-purpose tool into a domain expert for your business. By grounding LLM responses in your proprietary data, you get the reasoning power of GPT-4 combined with the accuracy of your internal knowledge base.