RAG vs Long Context for small organisations

  For small organizations , RAG (Retrieval-Augmented Generation) is often a better approach than relying solely on long-context LLMs because it delivers better accuracy, lower cost, and easier governance. Long Context Requires sending large amounts of data (hundreds of pages, sometimes millions of tokens) with every query. Token costs increase significantly as context size grows. Response latency also increases. Documents must be manually included in prompts. Applications need updates to ensure new content is supplied. RAG Only retrieves the most relevant documents or chunks. Sends a small subset of knowledge to the model. Reduces inference costs substantially. Add or update documents in the vector database/search index. New information becomes available immediately. For 100 docs — Long context is excellent — RAG is good For 10000 docs - Long context is good - RAG is excellent For 1million documents - Long context is impractical - RAG is Designed for this