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