RAG
Curated RAG tutorials.
Advanced RAG: Complete Guide 2026 – Beyond Basic Retrieval to Build Production-Grade Knowledge Bases
Basic RAG systems are easy to set up, but making them stable and effective in production is hard. This article dives deep into advanced RAG techniques: hybrid retrieval, reranking, multi-query decomposition, query routing, and systematic evaluation to improve RAG performance.
AdvancedContextual Compression RAG: Implementation Guide with Pinecone 2026
Contextual Compression RAG: Complete Implementation 2026 Overview Contextual Compression RAG is a specialized retrieval pattern that focuses on compressing retrieved context to fit LLM window. This guide shows you how to build a production-ready sy
IntermediateDify Enterprise Private Knowledge Base Complete Setup Guide: RAG Configuration & Best Practices (2026)
A detailed walkthrough of building an enterprise private knowledge base with Dify: Docker private deployment, document preprocessing strategies, chunk parameter tuning, embedding model selection, hybrid search configuration, and practical tips to fix common issues like 'irrelevant answers' or 'missing key information'.
IntermediateEnterprise Generative AI Adoption Playbook 2026: From Pilot to Scale
Enterprise generative AI playbook: use-case prioritization, build vs buy, RAG knowledge access, governance, cost structure, org models, and metrics.
IntermediateBuilding Enterprise-Grade RAG 2.0 Systems: A Complete Practice from Document Parsing to Knowledge Retrieval
This article systematically introduces the construction and optimization methods of enterprise-grade RAG 2.0 systems, covering key technologies such as document parsing, query rewriting, hybrid retrieval, ranking fusion, ontology constraints, and cache optimization. Combined with real-world scenarios in manufacturing and finance, it explains in detail how to address core challenges like parsing complex document structures, multi-turn dialogue anaphora resolution, and balancing retrieval precision and recall. It also introduces ontology-driven semantic constraints and caching mechanisms to improve accuracy and response efficiency in professional domains. Suitable for developers with basic RAG knowledge who want to build production-level systems.
AdvancedGraph RAG: Implementation Guide with Neo4j 2026
Graph RAG: Complete Implementation 2026 Overview Graph RAG is a specialized retrieval pattern that focuses on knowledge graph traversal for multi-hop reasoning. This guide shows you how to build a production-ready system using Neo4j. Why Graph RAG
AdvancedHybrid Search RAG: Implementation Guide with Elasticsearch 2026
Hybrid Search RAG: Complete Implementation 2026 Overview Hybrid Search RAG is a specialized retrieval pattern that focuses on combining vector and keyword search for maximum recall. This guide shows you how to build a production-ready system using
IntermediateLangChain vs LlamaIndex: Which Framework to Choose in 2025?
Comprehensive comparison of LangChain and LlamaIndex for building LLM applications. Compare architecture, use cases, performance, and ecosystem to make the right choice for your project.
IntermediateLangChain vs LlamaIndex 2026: Which Framework Should You Use for RAG?
Detailed comparison of LangChain and LlamaIndex for building retrieval-augmented generation applications in 2026. Covers architecture differences, performance benchmarks, integration ecosystems, and specific use cases where each framework excels.
AdvancedLangChain vs LlamaIndex vs Haystack: RAG Framework 2026
Detailed comparison of LangChain, LlamaIndex, and Haystack for building RAG pipelines. Covers document processing, retrieval strategies, performance benchmarks, and production deployment for 2026.
IntermediateLlamaIndex Practical Guide: RAG Application Development from Beginner to Production
LlamaIndex is purpose-built for RAG applications, making it the go-to framework for building enterprise knowledge base Q&A systems. This article covers the core architecture, key differences from LangChain, and 5 complete code examples from document loading to production deployment.
AdvancedLlamaIndex Tutorial 2026: Build Production RAG Applications
Complete LlamaIndex tutorial 2026. Covers VectorStoreIndex, persistent Qdrant storage, chat engines, sub-question decomposition, semantic chunking, metadata filtering, and streaming.
IntermediateLlamaIndex vs LangChain: Which One to Use for Building RAG (2026 Hands-On Comparison)
Everyone says LlamaIndex focuses on retrieval and LangChain leans toward orchestration, but when it comes to actual projects, you still get stuck. This article breaks it down by 'what you want to do,' with real code and pitfalls, helping you make a decision in 10 minutes.
AdvancedLLM Application Architecture Patterns: From Simple to Complex Systems
LLM application architecture patterns: RAG, agents/tool calling, model routing, caching, evals, guardrails, and observability — real patterns and trade-offs.
AdvancedReducing LLM Hallucinations: Practical Techniques for Production Applications
LLM hallucination—generating confident but false information—is the primary reliability challenge in production AI applications. This guide covers the root causes of hallucination, detection strategies (fact-checking layers, self-consistency checks, confidence calibration), mitigation techniques (RAG, constrained generation, chain-of-thought verification), and monitoring approaches for production systems. Includes benchmark data on hallucination rates across different model and technique combinations.
IntermediateMongoDB + Atlas Vector Search: How to Add AI search to MongoDB (2026)
MongoDB + Atlas Vector Search Integration Guide 2026 Overview This guide shows you exactly how to add AI search to MongoDB using MongoDB and Atlas Vector Search. We cover setup, core integration, and production-ready patterns. Prerequisites - Mon
AdvancedMulti-Vector RAG: Implementation Guide with Weaviate 2026
Multi-Vector RAG: Complete Implementation 2026 Overview Multi-Vector RAG is a specialized retrieval pattern that focuses on storing multiple embedding types per document. This guide shows you how to build a production-ready system using Weaviate.
IntermediateOpenAI Assistants API v2 2026: Files, Code Interpreter, and Threads
OpenAI Assistants API Status and Migration (2026): Officially deprecated, transitioning to Responses API. Provides concept mapping table (Thread → response chain / Run polling → direct return / vector store unchanged), five-step migration method, dual-run validation strategy, and the lesson that "managed state APIs should be abstracted and isolated."
AdvancedParent Document RAG: Implementation Guide with Chroma 2026
Parent Document RAG: Complete Implementation 2026 Overview Parent Document RAG is a specialized retrieval pattern that focuses on retrieving small chunks with large parent context. This guide shows you how to build a production-ready system using C
BeginnerPerplexity AI API Guide 2026: Real-Time Web Search for AI Apps
Complete Perplexity API guide. Covers sonar models, citations, streaming, multi-turn research, competitive intelligence, and hybrid web+private knowledge search.
Intermediatepgvector Tutorial 2026: Vector Similarity Search in PostgreSQL
pgvector tutorial (2026): Perform vector search on your existing PostgreSQL—HNSW vs IVFFlat selection, operator alignment, complete Python pipeline, SQL filtering and hybrid search (paid features in dedicated vector DBs are just a query here), memory estimation, and graduation thresholds.
AdvancedPython AI Development Stack 2026: FastAPI + LangChain + Supabase
Complete guide to building production AI applications with FastAPI, LangChain, and Supabase in 2026. Covers project setup, async AI endpoints, RAG pipeline, vector search, and deployment.
AdvancedSelf-Query RAG: Implementation Guide with Qdrant 2026
Self-Query RAG: Complete Implementation 2026 Overview Self-Query RAG is a specialized retrieval pattern that focuses on AI-generated metadata filters for precise retrieval. This guide shows you how to build a production-ready system using Qdrant.
IntermediateSemantic Search Implementation: Complete Developer Guide 2026
A complete guide to semantic search (2026): chunking → embedding → vector store → nearest neighbor search → re-ranking pipeline with real code, vector store selection (Chroma/Qdrant/pgvector/Pinecone), and quality levers like chunking, hybrid search, re-ranking, and metadata filtering. The retrieval backbone of RAG.
IntermediateSupabase AI Stack 2026: pgvector + Edge Functions + Realtime Streaming
Complete Supabase AI tutorial. pgvector for semantic search, Edge Functions for AI inference, real-time streaming, Row Level Security for user-scoped RAG, and a Next.js chat component.
IntermediateSupabase + pgvector: How to Add vector search to Supabase apps (2026)
Supabase + pgvector Integration Guide 2026 Overview This guide shows you exactly how to add vector search to Supabase apps using Supabase and pgvector. We cover setup, core integration, and production-ready patterns. Prerequisites - Supabase envi
IntermediateVector Databases Compared 2026: Pinecone vs Weaviate vs Qdrant vs Chroma
Comprehensive comparison of Pinecone, Weaviate, Qdrant, and Chroma vector databases for AI applications in 2026. Includes performance benchmarks, cost analysis, feature comparison, and recommendations for different use case categories.
AdvancedVector Databases & RAG in Production: Pinecone, Weaviate & pgvector in 2025
Retrieval-Augmented Generation (RAG) is the dominant pattern for grounding LLMs with up-to-date knowledge. This guide covers vector database selection (Pinecone, Weaviate, Qdrant, pgvector), embedding model selection and optimization, chunking strategies for documents, hybrid search (vector + keyword), re-ranking, evaluating RAG quality, and deploying production RAG systems that stay accurate over time.