Overview

• RAG pairs document retrieval with LLM generation to ground answers in a company’s own PDFs, databases and files instead of relying solely on pretraining.
• A step‑by‑step DEV guide shows a private, offline pipeline using Ollama with LlamaIndex, ChromaDB and the nomic‑embed‑text and Llama 3.1 models.
• Running locally offers privacy, zero per‑query API spend, offline use and easy model experimentation once models are downloaded.
• New analysis stresses persistent limits, including retrieval irrelevance, residual hallucinations, latency, complex debugging, operational overhead, monitoring needs and security controls such as access enforcement and vector store encryption.
• Emerging responses include selective fine‑tuning, agentic RAG orchestration, multimodal retrieval and use of evaluation/observability tools like TruLens and Ragas, alongside vector databases such as FAISS, Pinecone, Weaviate and Milvus.