Retrieval-augmented generation and knowledge systems: embeddings, vector databases, chunking strategies, and hybrid search. Learn how to ground LLM answers in your own data and cut hallucinations with better retrieval. Covers pipeline design, reranking, and evaluation for production RAG systems.
An operational comparison of recursive, semantic, section-aware, and late chunking for RAG ingestion, with guidance on which method fits which document type.
Why pure vector search misses exact product codes and names, and how to assemble the three-stage production baseline: parallel BM25 and dense candidate generation, Reciprocal Rank Fusion to merge the ranked lists, and a cross-encoder reranker for final ordering — implemented with Qdrant and Cohere.
Shipping one RAG deployment to many organizations forces a hard question: one shared Qdrant collection with tenant filters, or a separate collection per customer? This guide compares both isolation strategies, explains how Qdrant's filterable HNSW keeps tenant data fast and separated, and maps how…
Ragas doesn't just write questions — it builds a knowledge graph from your documents and then evolves simple prompts into reasoning, conditioning, and multi-context challenges. Here is what the generator LLM, the critic LLM, and the embedding model are actually doing inside testset generation, and…
Most engineers obsess over chunking strategies and vector databases, yet the pipeline that feeds them is where the real engineering lives. This guide maps all 11 stages of an automated docs ingestion pipeline — sitemap URL discovery, HTML cleaning, embedding, vector upserts, and incremental refresh…