GraphRAG, hybrid retrieval, hallucination prevention, citations, and vector databases. 14 articles, written by the engineers building AI App Lab.
Cross-encoder reranking for RAG: bi-encoder vs cross-encoder tradeoffs, BGE-reranker, Cohere Rerank, latency benchmarks, and implementation patterns for enterprise AI retrieval pipelines.
Stop AI hallucinations with RAG: grounded generation, citation verification, confidence scoring, retrieval quality, and seven techniques that reduce hallucination rates from 40% to under 5%.
AI citations for enterprise: fine-grained source attribution, citation verification, provenance chains, and audit trails for RAG systems. Why citations are a hallucination detection mechanism, not a UX feature.
RAG vs knowledge graphs: vector search finds documents, graph traversal finds connections. When to use each, when to combine them, and the hybrid architecture that enterprise AI actually needs.
Hybrid retrieval combines BM25 keyword search with vector semantic search for RAG. Reciprocal Rank Fusion, implementation patterns, and vendor comparison for enterprise AI.
GraphRAG combines knowledge graphs with RAG for multi-hop reasoning, global synthesis, and 3x accuracy on relationship queries. Architecture, indexing, query modes, and enterprise use cases.
Semantic answer caching for RAG: cache LLM responses by embedding similarity, not exact match. GPTCache, Redis, pgvector implementations. 25-50% hit rates, 100x latency reduction, 20-50% cost savings.
AI company knowledge base architecture: ingestion pipeline, hybrid retrieval, vector store, permissions, citations, and self-hosted deployment patterns for enterprise AI knowledge infrastructure.
A company brain is an AI-powered knowledge layer that ingests from all your tools, enforces permissions, and serves source-cited answers to humans and AI agents. Here's how it works.
Content hash deduplication in RAG ingestion: SHA256 document fingerprinting, chunk-level dedup, incremental indexing, and 20-40% embedding cost savings for enterprise AI pipelines.
Reciprocal Rank Fusion (RRF) combines rankings from multiple retrievers without score normalization. The formula, k parameter tuning, weighted variants, and implementation across Elasticsearch, OpenSearch, and pgvector.
Temporal knowledge graphs track how facts change over time with bi-temporal modeling. Valid time, transaction time, time-travel queries, and enterprise use cases for AI that knows when facts were true.
RAG query rewriting techniques: multi-query retrieval, HyDE hypothetical document embeddings, step-back prompting, and sub-query decomposition for improved retrieval accuracy in enterprise AI.
pgvector vs Pinecone vs Weaviate: performance benchmarks, pricing, hybrid search, scaling limits, and self-hosted vs managed tradeoffs for enterprise RAG vector databases in 2026.