Bi-encoder retrieval — the kind used in standard vector search — is fast but imprecise. It compresses each document into a fixed-size vector and scores query-document similarity independently, without either knowing anything about the other. Cross-encoders flip this: they see the query and document together and output a single relevance score. That joint view is … Read more
Building a RAG pipeline is straightforward. Knowing whether it actually works — and what specifically is broken when it does not — requires systematic evaluation. RAG systems can fail in at least four distinct ways: irrelevant retrieval, faithfulness failures where the LLM ignores the retrieved context, knowledge gaps where the right context is missing, and … Read more
Standard RAG retrieves once and generates once. Corrective RAG (CRAG) and Self-RAG add a step that standard pipelines skip entirely: evaluating whether the retrieved content is actually good enough before generating. Both architectures recognise that retrieval sometimes fails — the retrieved chunks are irrelevant, outdated, or insufficient — and build in mechanisms to detect and … Read more
RAPTOR, HyDE, and RAG Fusion are three advanced retrieval techniques that each address a different fundamental limitation of standard vector RAG. This guide gives each the depth it deserves: the core idea, why it works, when to use it, and a working implementation you can drop into an existing pipeline. RAPTOR: Retrieval at Multiple Levels … Read more
Chunking — splitting documents into pieces before embedding — is one of the most impactful decisions in a RAG pipeline, and one that receives far less attention than model choice or retrieval algorithms. The same documents chunked differently can produce dramatically different retrieval quality. This guide covers the three main strategies, when each works best, … Read more
ChromaDB is the right starting point for local RAG development, but production systems handling significant query volume, large document collections, or multi-user deployments often need more. Weaviate and Pinecone are two of the most capable production vector databases, each with a distinct positioning. This guide covers concrete setup and usage for both, with code you … Read more
Standard vector RAG retrieves semantically similar text chunks. GraphRAG instead builds a knowledge graph from your documents and traverses entity relationships to find relevant information. Multimodal RAG extends the pipeline to images, tables, and mixed-content documents. These are not replacements for vector RAG — they solve specific problems that vector RAG handles poorly. The Limits … Read more
Basic RAG — chunk, embed, retrieve top-K, generate — works well for straightforward document Q&A. But production RAG systems encounter harder queries that basic retrieval handles poorly: questions that span multiple documents, questions where the most semantically similar chunks aren’t the most relevant, questions requiring temporal or structural reasoning, and ambiguous queries where the user’s … Read more
