Everyone thinks RAG fails because models hallucinate. Actually: your chunks are dumb. If retrieval feeds garbage structure, generation can’t recover. Three upgrades: Semantic Chunking > Token Slicing 500-token splits ignore meaning boundaries. → Split by headings, sections, logical claims → Keep chunks 300–800 tokens max → Add 10–20% overlap for context continuity Payoff: Retrieval relevance improves 30–50%. Aha: Chunk size should match how humans think. Not tokenizer limits. ___ Connection-Aware Retrieval Most teams store chunks like isolated PDFs. But your data has relationships. Policies reference sections. APIs reference schemas. Research cites experiments. → Store metadata: author, version, section, entity → Use hybrid search: BM25 + embeddings → Re-rank top 20 → send top 5 Payoff: Answer accuracy jumps 2×. Latency barely changes. Aha: Retrieval isn’t about similarity. It’s about structure ___ The Knowledge Graph Layer Flat vector stores miss cross-document reasoning. Graphs preserve relationships. Instead of “find similar text” You ask: “What links A → B → C?” → Extract entities + relations during ingestion → Store triples alongside embeddings → Traverse graph, then retrieve supporting chunks Payoff: Multi-hop questions improve 3×. Think of it like this: Vectors = fuzzy memory. Graphs = connected memory. Best systems use both. Chunk smart. Store relationships. Retrieve with structure. 🔖 Save this for your next RAG architecture review 💬 Comment your struggles while building a RAG application ➕ Follow for more production-grade AI system design