RAG provides fast knowledge updates through re-indexing, and when paired with fine-tuning it delivers better enterprise tone and grounding.

Design prompts to force grounding in provided context, include a clear fallback if context is missing, and require source citations for auditability.

Phase One focuses on the indexing pipeline: ingesting documents from Confluence, SharePoint, and local files, with incremental syncing and thorough log checks to ensure coverage.

Store vectors in Weaviate for native hybrid search (dense vectors plus BM25) and multi-tenancy, with Qdrant or pgvector as scale- or need-dependent alternatives.

Be aware of common failure modes—retrieval failures, stale knowledge, and misweighted context—and monitor hit rates and re-ranking effectiveness.

Phase Two covers retrieval and generation: hybrid search for retrieval, cross-encoder re-ranking (ms-marco-MiniLM-L-6-v-2), and on-prem inference with Ollama using deterministic settings (temperature 0.1) for consistency.

Chunking strategy matters: prefer sentence- or paragraph-level chunks with windowed context to maintain coherence and precise retrieval; avoid crude 512-token splits.

Evaluate with the RAGAS framework, targeting faithfulness, relevancy, context recall, and context precision (faithfulness > 0.90 as an example target).

Use a single embedding model for both indexing and querying, prioritizing open-source, privacy-preserving options like BAAI/bge-large-en-v1.5 for local deployment.

Before shipping: establish a checklist covering performance targets, incremental re-indexing, fallback responses, and query logging for ongoing evaluation and trust.

Lead with the framing that LLMs alone can err; RAG grounds answers in the company knowledge base to ensure traceability, auditability, and up-to-date information.

High-level architecture: RAG comprises two pipelines—indexing and retrieval/generation—that share a single vector store to enable updatability without retraining.