06 section
Retrieval Systems
RAG end to end — chunking, embeddings, vector stores, hybrid search, reranking, graphs and agentic loops — with the failure modes that show up at each stage
What is in here
Follow the data: fundamentals first, then chunking and embeddings on the write path, then vector stores, hybrid search and reranking on the read path. GraphRAG, agentic RAG and the advanced patterns are what you reach for once a plain pipeline stops being accurate enough. Three topics appear twice here, once long-form and once as a shorter late-2025 update — the long version is placed first in each pair.
01
3 min
RAG Fundamentals
Grounding versus training as the core choice, the current RAG taxonomy, and where a two-million-token context window makes retrieval unnecessary — and where it does not
ragretrievalfundamentals
02
3 min
Chunking Strategies
Small chunks find, large chunks explain — resolve that tension with recursive structure splitting, semantic boundaries, and parent-child hierarchies tuned per content type
chunkingragretrieval
03
3 min
Embedding Models
Choosing an embedding model in the Matryoshka and ColBERT era, where truncated dimensions and binary quantization shrink an index twentyfold for under two percent recall
embeddingsretrievalmodel-selection
04
11 min
Vector Databases
The full operating manual for vector stores: HNSW and IVF index tuning, metadata filtering, upserts and reindexing, cost modelling, and a selection framework
vector-dbretrievalproduction
05
3 min
Vector Database Comparison
A vendor-by-vendor read on Pinecone, Qdrant, Weaviate, Milvus and pgvector, scored on scale, filtering, tenancy and the real cost of managed versus self-hosted
vector-dbretrievalcost
06
10 min
Hybrid Search
The long-form treatment of dense plus sparse retrieval: query-type analysis, fusion by RRF or weighted scores, tuning alpha, and running both indexes in production
searchretrievalrag
07
3 min
Hybrid Search in Brief
A condensed pass on fusing keyword and vector results: where dense retrieval misses part numbers, how RRF works, and what learned-sparse SPLADE changes
searchretrievalembeddings
08
9 min
Reranking
Second-stage precision in depth: cross-encoder architectures, hosted rerankers, LLM listwise scoring, latency budgets, and deciding which queries are worth reranking at all
rerankingretrievalproduction
09
3 min
Reranking Strategies
The bi-encoder to cross-encoder funnel in brief, with listwise LLM reranking and distilled small-model rerankers weighed against the milliseconds they add per query
rerankingretrievallatency
10
3 min
GraphRAG
When the question spans the whole corpus rather than one chunk: entity extraction, community summarization, relationship traversal, and the indexing cost that buys it
knowledge-graphragretrieval
11
3 min
Agentic RAG
Turning the retrieval pipeline into a loop the model controls — Self-RAG reflection, corrective re-retrieval, multi-hop chains, and the latency variance that comes with them
ragagentsreasoning
12
3 min
Advanced Retrieval Patterns
The techniques that close the query-document gap: multi-query decomposition, HyDE, Anthropic-style contextual retrieval, iterative enrichment, and in-context reranking
retrievalragsearch