<\/span><\/h2>\nHigher recall can introduce more irrelevant evidence. Aggressive filtering can improve precision but hide content with incomplete metadata. Reranking can improve ordering while increasing latency.<\/p>\n
Stricter abstention reduces unsupported answers. However, an overly cautious system may frustrate users. Tune thresholds by workflow risk and provide a clear escalation path.<\/p>\n
Detailed tracing improves diagnosis but may capture sensitive queries or content. Apply retention rules, encryption, access restrictions, and redaction to observability data.<\/p>\n
Caching reduces latency and cost. Yet cached answers can violate freshness or permission changes. Bind cache entries to source versions and authorization context.<\/p>\n
Managed services reduce infrastructure burden. In contrast, custom systems may offer more control over retrieval and governance. Compare operational ownership, portability, integration depth, and evaluation access.<\/p>\n<\/section>\n\n<\/span>Practical Next Steps: A 30-Day Production RAG Plan<\/span><\/h2>\n<\/span>Week 1: Baseline the current system<\/span><\/h3>\n\n- Name an owner for ingestion, retrieval, security, generation, and evaluation.<\/li>\n
- Collect 40 to 100 representative questions from the target workflow.<\/li>\n
- Label expected evidence and expected behavior for each case.<\/li>\n
- Run the baseline and classify every failure using the taxonomy.<\/li>\n<\/ul>\n
<\/span>Week 2: Repair the highest-volume upstream failures<\/span><\/h3>\n\n- Fix stalled connectors, parser errors, duplicate sources, and missing metadata.<\/li>\n
- Add document status, effective dates, owners, and permission attributes.<\/li>\n
- Review chunk boundaries around the most important failed questions.<\/li>\n
- Retire or demote superseded content through explicit rules.<\/li>\n<\/ul>\n
<\/span>Week 3: Tune retrieval and safe behavior