RAG works beautifully when your content is clean, retrieval is precise, and answers are grounded. It fails when PDFs are messy, chunks are arbitrary, and nothing is evaluated. Here’s a production‑ready checklist.
Ingestion and indexing
Use structured parsers for PDFs, slides, and HTML. Normalize headings, tables, and links. Generate stable IDs so you can track content lineage and update individual chunks without rebuilding the entire index.
Chunking that respects meaning
Prefer semantic chunking with small overlaps (for example 200–500 tokens with 10–15% overlap). Tune with evaluation: bigger isn’t always better. Store metadata like source URL, section, and last‑modified.
Retrieval and generation
Use hybrid retrieval—sparse (BM25) + dense embeddings—with reranking. Consider query rewriting to capture user intent. During generation, include inline citations and short quotes to show grounding, and cap the number of sources to keep answers crisp.
Freshness and governance
Schedule re‑indexing of changed sources and alert on parser failures. Maintain allowlists for trusted domains and redact sensitive fields at ingestion time.
Evaluation loop
Build a golden set from real questions. Rate answers for accuracy, grounding, and completeness; track latency and cost. Re‑run after content or embedding model updates to catch regressions. Watch for drift and data quality issues.
Do these basics well and your RAG system will be fast, cheap, and—most importantly—trustworthy.