5. HYBRID RAG + KG SYSTEMS (MAIN FOCUS)

(This is it. This is why you're here. RAG alone is useful but limited. Knowledge graphs alone are powerful but rigid. Combined correctly, you get a system that can answer questions neither approach could handle alone. Combined incorrectly, you get twice the complexity and worse results than either system alone. Pay attention to the failure modes in this section - they're drawn from real production systems that had to be rebuilt.)

Why Combine RAG and Knowledge Graphs?

Limitations of Pure RAG

❌ Struggles with multi-hop reasoning

Question: "What technology does Alice's manager's company use?"
Pure RAG: Retrieves documents mentioning Alice, managers, companies, technology separately
→ No coherent answer

❌ Misses structured relationships

Question: "Who reports to the CTO?"
Pure RAG: Finds documents with "CTO" and "reports"
→ May miss implicit reporting structures

❌ No entity disambiguation

Question: "What does Apple produce?"
Pure RAG: Returns info about fruit OR company
→ No context to disambiguate

Limitations of Pure KG

❌ Can't handle unstructured knowledge

Question: "What are best practices for API design?"
Pure KG: No nodes for "best practices" concept
→ Can't answer without structured triples

❌ Limited by schema

Question: "What did the CEO say in the Q4 earnings call?"
Pure KG: Doesn't store full transcript text
→ Only has structured metadata

Power of Hybrid RAG + KG

✅ Multi-hop reasoning (from KG) + Rich context (from RAG) ✅ Structured queries (KG) + Semantic search (RAG) ✅ Entity disambiguation (KG) + Document retrieval (RAG) ✅ Explainable paths (KG) + Cited answers (RAG)