Retrieval-Augmented Generation (RAG)
15 articles · written by Niraj Kumar
Retrieval-Augmented Generation is the pattern behind most useful LLM products: instead of fine-tuning a model on your data, you retrieve the relevant passages at query time and put them in the prompt. It is easy to demo and hard to ship — the difficulty is never the LLM call, it is chunking, retrieval quality, tenant isolation, and knowing when your answers have quietly degraded.
These articles work through that gap. They cover the ingestion side (document parsing, chunk sizing, embedding choice), the retrieval side (pgvector similarity search, PostgreSQL full-text search, Reciprocal Rank Fusion for hybrid scoring, rerankers, and graph traversal for multi-hop questions), and the operational side (per-tenant isolation with Row-Level Security, cost attribution, and evaluation harnesses that catch regressions before users do).
Every guide is written against a concrete stack rather than pseudocode — mostly PostgreSQL with pgvector, LangChain or LangGraph, and Next.js or Node.js — and each one names the trade-off it is making.
New to the topic? Start with the RAG Architecture Guide — the end-to-end overview these tutorials build on.
- PostgreSQLCDCSemantic Cache
How to Prevent AI Context Poisoning using PostgreSQL CDC for Real-Time Semantic Cache Invalidation in Node.js
Learn how to stop stale semantic caches from poisoning your AI agents. Build a real-time invalidation pipeline with PostgreSQL CDC, a Node.js worker, and an embedding similarity radius to flush outdated cache entries instantly.
- AI AgentsNode.jsVector Database
How to Build an Agentic Memory Garbage Collector in Node.js to Prevent Vector Store Context Poisoning
Learn how to build a Node.js memory garbage collector for AI agents. Implement time-weighted relevance decay, semantic deduplication, and safe pruning to stop vector store context poisoning and hallucination cascades.
- pgvectorPostgreSQLNode.js
How to Sync pgvector Embeddings in Real-Time using PostgreSQL Triggers and Node.js Background Workers
Stop relying on cron jobs for vector search. Learn how to build a real-time, event-driven pipeline using PostgreSQL triggers, an outbox table, and isolated Node.js background workers to keep pgvector embeddings fresh without locking your production database.
- AI SecurityPrompt InjectionNode.js
How to Build a Zero-Trust AI Sanitization Middleware in Node.js to Prevent Indirect Prompt Injection
Learn how to build a zero-trust sanitization middleware in Node.js and TypeScript using Express and js-tiktoken to defend against indirect prompt injection, enforce hard token limits, and stop untrusted RAG data from hijacking agentic tool calls.
- RAGRagasOllama
How to Automate RAG Regression Testing and Hallucination Checks in GitHub Actions Using Ragas and Ollama
Learn how to automate RAG regression testing in GitHub Actions. Run Ollama in CI, score pull requests with Ragas (faithfulness, answer relevancy, context recall), and block merges that introduce hallucinations or retrieval drift.
- AI SecurityPrompt InjectionNext.js
How to Protect Your AI Application from Prompt Injections using Llama Guard and Next.js Edge Middleware
A practical 2026 guide to securing RAG and LLM applications against prompt injection and jailbreak attacks using Llama Guard, Ollama, Next.js Edge Middleware, and PostgreSQL-based threat logging aligned with the OWASP Top 10 for LLMs.
- RAGPostgreSQLNext.js
How to Build a Secure Multi-Tenant RAG Pipeline using PostgreSQL Row-Level Security, Next.js, and JWT Auth
Stop cross-tenant leaks in B2B AI SaaS — PostgreSQL Row-Level Security for embeddings, tenant IDs from Next.js JWTs, and isolated retrieval.
- GraphRAGLangChainOllama
How to Build a Local GraphRAG Pipeline with PostgreSQL, LangChain, and Ollama for Multi-Hop Reasoning
Build a fully local GraphRAG system with LangChain, Ollama and PostgreSQL — extract entity triplets, store a knowledge graph, run multi-hop queries.
- pgvectorPostgreSQLRAG
How to Implement Hybrid Search with pgvector and Full-Text Search in PostgreSQL for Node.js RAG Pipelines
Combine pgvector embeddings with PostgreSQL full-text search using Reciprocal Rank Fusion for accurate, hallucination-resistant RAG in Node.js.
- RAGOllamaLangChain
How to Build a Local RAG Pipeline Using Ollama, LangChain, and PostgreSQL
Set up a private, offline RAG system by connecting locally hosted LLMs via Ollama to a PostgreSQL pgvector database using LangChain.
- RAGLangChainNext.js
Building a Production-Ready RAG System with LangChain and Next.js
Architect and build a production RAG system with LangChain for orchestration and Next.js for the UI — ingestion, retrieval, streaming, deployment.
- LangChainOpenAIOllama
Building Your Own Chatbot with LangChain and OpenAI (or Ollama)
Build a production chatbot with LangChain and OpenAI or Ollama — document ingestion, vector stores, a RAG pipeline, and a deployable web interface.
- Graph-RAGKnowledge GraphsRAG
Graph‑RAG Explained: Building Smarter AI Agents with Knowledge Graphs
How Graph-RAG combines knowledge graphs with LLMs to improve multi-hop reasoning and accuracy — with code examples and enterprise use cases.
- LangChainLlamaIndexLLM
LangChain vs. LlamaIndex: Which One Should You Use in 2026?
LangChain vs LlamaIndex compared on architecture, use cases and developer experience — with code examples to help you pick an LLM framework.
- RAGAILLM
Mastering RAG in Practice: Build a Production-Ready AI Search Feature for Your App
Build a production RAG system from scratch — architecture patterns, code examples, and what it takes to ship intelligent search inside your app.
Need this built rather than explained?
Custom RAG chatbots with LangChain, pgvector/Pinecone, and citation tracking.
RAG Chatbot Development →Related topics
- LangChain & LangGraph
- Large Language Models
- AI Agents
- Next.js
- React
- TypeScript
- Node.js
- PostgreSQL
- Docker & Containers
- DevOps
- CI/CD
- Application Security
- AWS
- n8n Automation