Corrective RAG and Self-RAG: Agentic Retrieval Architectures Explained
Standard RAG retrieves once and generates once. Corrective RAG (CRAG) and Self-RAG add a critical missing step: evaluating whether the retrieved content is actually good enough before generating an answer. Both architectures recognise that retrieval sometimes fails — the retrieved chunks are irrelevant, out of date, or insufficient — and build in mechanisms to detect … Read more
RAPTOR, HyDE and RAG Fusion Explained: Three Advanced Retrieval Techniques
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
Document Chunking for RAG: Fixed vs Semantic vs Parent-Child Strategies
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
How to Use Weaviate and Pinecone for Production RAG
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
GraphRAG vs Vector RAG: Differences, When to Use Each and Multimodal RAG
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
Advanced RAG Techniques 2026: Reranking, HyDE, Parent-Child and 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
How to Use ChromaDB with LangChain and Build RAG Without LangChain
LangChain wraps ChromaDB (and most other vector databases) behind a unified interface, making it easy to swap components and prototype quickly. But LangChain adds abstraction overhead, makes debugging harder, and changes frequently enough that tutorials become outdated quickly. This guide covers both approaches: the LangChain integration for teams already invested in the ecosystem, and the … Read more
How to Build a Local RAG Pipeline with Ollama and ChromaDB
RAG (Retrieval-Augmented Generation) lets you give a language model access to your own documents — PDFs, notes, wikis, code — without fine-tuning. The model answers questions using content retrieved from your document store rather than only its training data. This guide builds a complete local RAG pipeline: Ollama for LLM inference, ChromaDB for vector storage, … Read more