Answer RAG interview questions in four steps: the three-stage pipeline, where each stage fails, which of nine variants patches which hole, and the 6 kinds of problems RAG cannot solve. Self-RAG, CRAG and Adaptive-RAG add judgment; Graph RAG and KG-RAG change the index; CAG skips retrieval; Agentic RAG and Reasoning RAG hand decisions to the model.
Same 'knowledge graph + retrieval' label, three different bets: Microsoft GraphRAG v3.1.2 pays indexing cost for global summarization, LightRAG cuts cost with dual-level retrieval and incremental updates, HippoRAG 2 turns RAG into growing associative memory via PPR — this guide splits the trade-offs by component with four query modes, indexing pipelines, and a selection matrix.
Cognee is a data-to-memory pipeline: a relational store preserves sources and provenance, a vector store finds semantically similar content, and a graph store represents entity relationships, exposed through remember, recall, improve, and forget.
SPINACH does not guess complete SPARQL in one shot. It searches entities and properties, inspects Wikidata entries and examples, executes small queries, and composes a final query under explicit action and stopping rules.
Zep does not merely vectorize chat history. It turns episodes into entities and facts with validity time, allowing new information to invalidate an old relationship without erasing history. Graphiti is the open-source framework; Zep adds managed scale and governance.
CS224W is Stanford's machine learning with graphs course: nineteen lectures from node embeddings, GNNs, graph transformers and knowledge graphs to relational deep learning and GNNs with LLMs. The slides, three written homeworks and six Colabs are public and run as downloaded; the public recordings date from 2021, so the newer topics in the second half exist only as slides, and the exam and autograding are out of reach.
Prevalent AI closes a $22M first institutional round led by Integrity Growth Partners. The deal shows that 'prove the market first, raise later' still works in the agentic AI era — while most startups burn VC money searching for PMF, a company that bootstrapped for 9 years and already serves large enterprises chose to raise only when agentic AI needs it most.
CodeGraph uses tree-sitter to extract a codebase into a local SQLite/FTS5 knowledge graph, letting AI coding agents query the graph instead of scanning files. The official end-to-end benchmark (7 repos, median of 4 runs) averages 35% cost savings and 70% fewer tool calls -- but only if the agent actually walks the graph. Delegating exploration to a file-reading subagent that ignores CodeGraph turns it into pure overhead.
Graphify uses tree-sitter AST to extract code structure, then applies LLM semantic analysis to documents and images, compressing an entire project into a queryable knowledge graph. It claims to save 71.5x tokens per query compared to reading raw files.
code-review-graph uses Tree-sitter to parse your codebase and build a persistent knowledge graph, tracks the blast radius of changes, and feeds only truly relevant context to the AI — claiming an average 8.2x reduction in token usage.
Vector search finds similarity; graph search traverses relationships. When a question requires reasoning across multiple entities — crag → route → sender → grade distribution — GraphRAG outperforms standard RAG.