LLM Hub — RAG & Retrieval Systems

RAG & Retrieval Systems

Embedding models, vector databases, hybrid search and reranking (plus two deep-dive companions on the two-step cascade architecture and the underlying reranker mathematics), chunking, agentic RAG, GraphRAG, and what it actually takes to ship retrieval to production.

EmbeddingsVector DBHybrid SearchRerank MathCascadeGraphRAG

Presentations in This Series

  1. Embedding Models →
    Dense vs sparse; bi-encoder vs cross-encoder; modern families (BGE, GTE, E5, Voyage, OpenAI text-embedding-3, Cohere, Nomic, Jina); ColBERT late interaction; Matryoshka; domain adaptation.
    live
  2. Vector Databases →
    pgvector, Qdrant, Weaviate, LanceDB, Pinecone, Milvus, Chroma; HNSW, IVF, PQ; recall vs latency; updates and durability; decision matrix.
    live
  3. Hybrid Search & Reranking →
    BM25 mechanics; RRF and weighted fusion; cross-encoder rerankers (BGE-Reranker, Cohere Rerank, mxbai); ColBERT-as-reranker (PLAID); cascades; latency budgets.
    live
  4. Chunking & Ingestion →
    Strategies (fixed, semantic, structural, contextual); recursive splitting failure modes; layout-aware (Unstructured, MinerU, LlamaParse, Docling); ColPali for vision; deduplication.
    live
  5. Agentic RAG Patterns →
    HyDE, query decomposition, Self-RAG, Corrective RAG (CRAG), iterative retrieval, query routing, tool-using RAG.
    live
  6. GraphRAG & Knowledge Graphs →
    Microsoft GraphRAG, KG construction, entity resolution; Neo4j, Memgraph, KuzuDB; hybrid graph+vector retrieval; Cypher in agent loops.
    live
  7. Production RAG →
    RAGAS, ARES, Trulens; drift & freshness; latency budgets; multi-tenancy and ACLs; citation & grounding; cost; failure modes.
    live
  8. Two-Step Retrieval — Architecture & Cost-Quality Mathematics →
    Companion deep-dive to deck 03. The bi-encoder / cross-encoder asymmetry; the indexability barrier (why MIPS scales and cross-attention doesn't); ANN math (HNSW, IVF, PQ recall–latency curves); the recall-ceiling theorem; RRF derivation; latency composition under tails; the (k, quality, latency) Pareto frontier; Platt / isotonic / temperature calibration; worked example at 10 M docs / 200 ms.
    live
  9. Reranker Mathematics →
    Stage-2 maths in depth. Cross-encoder attention & token interactions; ColBERT MaxSim formal definition; learning-to-rank families (pointwise / pairwise / listwise); RankNet sigmoid loss and gradient; LambdaRank weighted by |ΔNDCG|; LambdaMART GBDT; InfoNCE contrastive loss with hard negatives; MarginMSE cross-encoder → bi-encoder distillation; DCG / NDCG / MRR / MAP derivations.
    live