Foundation Model RAG Integration — Knowledge-Grounded AI

I will build a RAG-augmented foundation model system — grounding your foundation model's responses in your organisation's specific knowledge base rather than its general training data, covering document ingestion, vector indexing, retrieval integration, source citation, and a confidence-based fallback when the knowledge base doesn't contain relevant information. The core limitation of using a frontier foundation model without RAG is that the model's training knowledge is general, static, and doesn't include your organisation's proprietary information — RAG augmentation gives the model access to your specific, current knowledge while keeping the generation quality and reasoning capability of the foundation model.

The system covers knowledge base ingestion, vector database setup, retrieval integration into the foundation model's context, source citation in responses, confidence scoring, graceful fallback for out-of-knowledge queries, and deployment as an API or chat interface.