Designing a fully local RAG with small language models setup

Modern AI architectures often rely on large language models (LLMs) hosted externally, which can be problematic for enterprises with strict data privacy and locality requirements. This article discusses a local-first approach using small language models (SLMs) and retrieval-augmented generation (RAG) to address these constraints effectively. By employing a fully local architecture, sensitive internal data remains private, and AI systems can still support tasks like querying documentation, triaging incidents, and generating structured outputs. The architecture separates tasks into intent detection, local retrieval, and task-specific reasoning, all executed on modest on-premise hardware, ensuring privacy and operational efficiency. The approach is demonstrated through a fictional nuclear facility use case, showcasing how privacy-critical environments can benefit from this setup without relying on cloud-based services. This local-first architecture allows enterprises to maintain control over data and AI processes while reducing the risk of hallucinations and ensuring responses are grounded in actual documentation.