GraphRAG

A Practitioner's Guide to Graphs - Tim Ainge, Good Collective

Jul 18, 2026 · 14:18

Tim Ainge from Good Collective presents a practitioner's guide to graphs, covering extraction from unstructured text, schema-first design, and graph-native algorithms to make AI applications smarter, cheaper, and more reliable. He demonstrates that giving extractors a schema (e.g., recipe with ingredients and steps) yields more meaningful graphs, and using ontology instructions standardizes units and ingredient names. Embedding models solve the potato–potato problem by flexibly matching duplicate nodes. Personalized PageRank, inspired by Pinterest's Pixie paper and HIPORAG, finds authoritative nodes in dense graphs, e.g., identifying Miranda v. Arizona as a landmark case not directly cited. Shortest path algorithms reduce tool calls by 40% in code search by retrieving intermediate nodes missed by vector search. Subgraph matching detects software design patterns like the decorator by querying graph shape without specific node details.

Stop AI Agent Hallucinations: 5 Techniques + Production Patterns - Elizabeth Fuentes, AWS

Jul 11, 2026 · 55:19

Elizabeth Fuentes (AWS) presents five code-based techniques to stop AI agent hallucinations, each with measurable before/after metrics. Semantic Tool Selection filters 29 tools to the 3 most relevant per query, cutting token usage from thousands to under 300 per call. Graph-RAG replaces vector similarity with structured graph queries (using Neo4j), enabling precise aggregation and multi-hop reasoning that vanilla RAG fabricates. Multi-Agent Validation uses an Executor-Validator-Critic swarm to catch fabrications, achieving a 92% detection rate. Neurosymbolic Guardrails enforce business rules in Python hooks that the agent cannot skip, achieving zero rule violations. Agent Steering guides agents to self-correct when soft rules fire, completing tasks without hard failures—demonstrated by booking 50 guests by intelligently splitting into two rooms.

Layering every technique in RAG, one query at a time - David Karam, Pi Labs (fmr. Google Search)

Jul 29, 2025 · 20:22

David Karam of Pi Labs (formerly Google Search) walks through layering every technique in RAG, from in-memory retrieval to planet-scale search with 70+ corpus mix of token, embeddings, and knowledge graphs, jointly retrieved and re-ranked at 160,000 queries per second in under 200msec. He advocates a Quality Engineering Loop: baseline with simplest methods, analyze losses, then apply incremental techniques based on complexity-adjusted impact. Karam explains why queries like "falafel" are notoriously hard due to ambiguous intent, highlights failures of chunking documents, and shows when BM25 suffices, when relevance embeddings are needed, and when custom embeddings or domain-specific signals (price, popularity, user clicks) become essential. He covers query orchestration via fan-out, supplementary retrieval across back ends, and distillation for cost optimization, concluding that at sufficient complexity, problems must be punted to LLM or UX for graceful degradation.