1️⃣ Source Classification Layer A. Reliability Tiering Tier 1 (Authoritative, Structured, High Trust) API (Pricing & Inventory – Real-time) Compliance PDFs (Legal-approved) Tier 2 (Operational Knowledge) Notion SOPs (Team-managed) Tier 3 (Contextual / Marketing) Website pages Support docs B. Freshness Index Real-time: API Weekly: Notion, Website Static: PDFs C. Namespace Design compliance.* operations.* marketing.* realtime.inventory.* support.* Each namespace enforces metadata tagging: source_type timestamp version authority_score freshness_score --- 2️⃣ Query Routing Logic Step 1: Query Intent Detection 1. Pricing / availability → Route to realtime.inventory.* 2. Policy / regulation → Route to compliance.* 3. How-to / process → Route to operations.* 4. Promotional / feature description → Route to marketing.* 5. Multi-domain query → Trigger composite routing Priority Rules Real-time queries always call API first Compliance overrides marketing if conflict detected Operational SOP overrides support articles Parallel vs Sequential Retrieval Parallel: When multi-step query spans multiple namespaces Sequential: When API confirmation is required before deeper reasoning --- 3️⃣ Cross-Source Relevance Scoring Hybrid Ranking Formula Final Score = (Semantic Similarity × 0.5) + (Authority Score × 0.2) + (Freshness Score × 0.2) + (Context Overlap Score × 0.1) Confidence Weighting API: 0.95 base confidence Compliance PDFs: 0.90 Notion SOPs: 0.80 Website: 0.65 Support Docs: 0.60 Context Overlap Detection Cross-document entity matching Temporal alignment check Version consistency comparison --- 4️⃣ Conflict Resolution Framework Conflict Handling Logic 1. Detect semantic contradiction 2. Compare authority_score 3. Compare freshness_score 4. Apply tie-break rule: Regulatory > Operational > Marketing Escalation Triggers Equal authority and conflicting timestamps High business critical query + low confidence Repeated conflict pattern within 24 hours Escalation Path: Flag for human validation Log to conflict monitoring system --- 5️⃣ Latency Optimization Strategy Source Batching Pre-batch compliance + operations embeddings API called only when intent detected Cache Strategy Cache: Frequent operational queries (TTL 24h) Compliance answers (TTL 7 days) No cache for real-time inventory Fallback Thresholds If API > 1.5s delay → Return cached last-known value with disclaimer If secondary retrieval > 3s → Return partial answer + async enrichment --- 6️⃣ Freshness & Update Handling Real-Time Handling API always live call Metadata stamped at response time Incremental Indexing Workflow 1. Detect document change 2. Chunk-level diffing 3. Re-embed only changed segments 4. Update namespace index Stale Data Detection Timestamp decay scoring If age > threshold → Lower freshness_score dynamically --- 7️⃣ Monitoring & Evaluation Metrics Retrieval Precision Top-3 relevance match rate Query-to-source accuracy % Source Performance API latency average Namespace hit rate Retrieval success rate Conflict Monitoring Conflict frequency % Escalation rate False conflict detection rate --- 8️⃣ Risk & Failure Analysis Over-Retrieval Risk Too many low-authority docs dilute signal Mitigation: Minimum authority threshold Top-k dynamic adjustment Under-Context Risk Missing operational nuance Mitigation: Context expansion step if confidence < 0.75 API Dependency Risk Downtime Rate limits Mitigation: Graceful degradation Cached fallback layer --- 9️⃣ Scalability Roadmap Phase 1: Controlled Multi-Source Setup Manual routing rules Static authority weights Basic conflict resolution Phase 2: Dynamic Routing Intelligence Query classifier refinement Adaptive authority weighting Automated conflict detection clustering Phase 3: Adaptive Self-Improving Orchestration Feedback loop from user corrections Reinforcement scoring adjustments Predictive source pre-fetching --- 🔟 Executive Orchestration Summary Core Routing Principle Route based on intent → Rank by authority → Adjust by freshness → Validate with conflict logic. Highest Risk Factor Cross-source contradiction under high-criticality queries. One Improvement for Retrieval Precision Implement adaptive authority re-weighting based on historical correctness feedback. --- System Outcome This multi-source RAG system ensures: Right source selection through intent-aware routing Reliable answers via authority-weighted scoring Controlled latency through smart batching Conflict resilience through structured resolution Designed to retrieve the right information from the right source at the right time.