RAG Done Right: Making Your Private Knowledge Searchable
Why RAG
RAG reduces hallucinations and keeps sensitive data in‑house while boosting search quality.
Core Architecture
- Ingestion: connectors, PII scrubbing, versioning.
- Chunking: semantic boundaries over fixed tokens.
- Indexing: embeddings, metadata, hybrid search.
- Retrieval: reranking, filters, top‑k tuning.
- Generation: prompts, tools, grounding citations.
Evaluation
- Build ground truth Q&A sets and track retrieval precision, faithfulness, and latency.
Common Pitfalls
- Over‑chunking, noisy metadata, and missing evals.
- Ignoring permissions during retrieval.
