Beyond Chatbot Prototypes
Enterprise AI deployments fail when models hallucinate facts or leak confidential corporate data. Building production-grade Retrieval-Augmented Generation (RAG) requires structured chunking strategies, hybrid keyword-vector retrieval, and strict safety guardrails.
The Enterprise RAG Pipeline
- Context-Aware Document Chunking: Parse documents into semantically coherent sections with metadata tagging rather than arbitrary character splits.
- Hybrid Retrieval: Combine dense vector embeddings (OpenAI text-embedding-3) with sparse BM25 keyword matching to retrieve domain-specific acronyms reliably.
- Reranking for Precision: Run candidates through a cross-encoder reranker (Cohere Rerank) to present only the top 3 most relevant passages to the LLM context window.
- Output Evaluation & Guardrails: Apply NeMo Guardrails and LangSmith evals to filter prompt injection attempts and enforce grounded citations.