// TOPIC
#embeddings
3 articles
◆◆◆AdvancedMicrosoftNeo4j
01Design a GraphRAG System (Knowledge-Graph-Augmented Retrieval)
When vector RAG fails on multi-hop and corpus-wide questions, build a GraphRAG knowledge graph: entity extraction, Leiden communities, map-reduce global search.
#interview#ai#rag
29 min◆◆◆AdvancedPineconeGoogle
02Design a Vector Database / Semantic Search Service
Index 1 billion 768-dimensional vectors and answer top-k similarity queries in under 20 ms — the ANN indexing, sharding, and filtering architecture behind Pinecone, Weaviate, and pgvector.
#interview#ai#vector-db
25 min◆◆◆AdvancedOpenAIGoogle
03Design a RAG (Retrieval-Augmented Generation) Pipeline
Design a RAG pipeline that grounds an LLM in 10M documents with sub-2-second, cited answers: ingestion, hybrid retrieval, reranking, generation, and eval.
#interview#ai#rag
30 min