RAG

Build a Self-Correcting RAG Agent with LangGraph and Milvus

Wire adaptive routing, corrective RAG, and self-RAG into a stateful LangGraph agent backed by Milvus Lite — no OpenAI key required.

Hybrid Search with RRF in Elasticsearch: A Complete Guide

Combine BM25 and kNN vector search using Reciprocal Rank Fusion in Elasticsearch to build a production-ready RAG retriever in Python.

Build Persistent Agent Working Memory with MongoDB and Tavily

Store, retrieve, and grow agent memory across sessions using MongoDB Atlas Vector Search and Tavily hybrid RAG with save_foreign.

Build a Multi-Modal RAG Pipeline with LlamaIndex and Qdrant

Index text and images into separate Qdrant stores, retrieve across both modalities, and answer queries with GPT-4 Vision using LlamaIndex.

The Complete Developer Guide to Running LLMs Locally: From Ollama to Production

Everything you need to run LLMs on your own hardware in 2026: VRAM sizing, model formats, an 8-tool comparison table, a full local RAG pipeline, and Docker production deployment with GPU passthrough and Nginx auth.

How to Run DeepSeek R1 Locally with Ollama: Full Setup Guide

Install DeepSeek R1 locally using Ollama in under 5 minutes. Covers model variant selection from 1.5B to 671B, visible chain-of-thought reasoning, REST API usage, Python integration, and building a simple RAG application.

Mastering Advanced RAG Evaluation: From Basic Metrics to LLM-as-a-Judge

Beyond Prompt Engineering: Deep-Dive RAG for Intent Detection & Slot Filling in AI Agents

Stop relying on massive, static context prompts. Learn how to combine Retrieval-Augmented Generation (RAG), vector search (FAISS), and history compaction to achieve scalable intent detection and slot filling.

Building a Production RAG Pipeline with Bedrock and OpenSearch Serverless

Everyone has shipped a RAG demo. Shipping one that survives real traffic, security audits, and finance reviews requires a different architecture. Explore the enterprise-grade RAG stack on AWS.

Secure RAG on Google Cloud: From Private Data to Safe Answers

Production RAG is not just a way to 'chat with your docs'—it is a high-risk data access system. Learn how to build a secure RAG architecture on GCP using Vertex AI, Model Armor, and VPC Service Controls.

Learning to Rank: The Hidden Layer Powering Modern RAG

Your RAG pipeline is only as good as your ranker. Explore the technical depths of Learning to Rank (LTR), from LambdaMART's 'Physics Trick' to the Cross-Encoder tradeoff that determines production success.

Designing a Production-Grade RAG Architecture

Large Language Models are powerful—but infamously unreliable when forced to guess. Learn how to build a production-grade RAG architecture that eliminates hallucinations through hybrid search, reranking, and structured ingestion.

RAG vs. MCP: What Every AI Developer Actually Needs to Know

Retrieval-Augmented Generation (RAG) and Model Context Protocol (MCP) are often discussed as competitors, but they solve entirely different problems. Learn when to reach for memory and when to reach for agency.

How to Build a Time-Series RAG for Forecasting Data

Standard RAG is for text; Time-Series RAG is for numbers. Learn how to combine LLMs with historical patterns, statistical insights, and LangGraph to build more accurate forecasting systems.

How to Build a Custom RAG System with Pinecone and Next.js

A step-by-step developer guide to converting your proprietary PDFs into an intelligent, searchable chatbot using Vector Databases.

Getting Started with LangChain in 2026

A comprehensive tutorial on building your first RAG application using the latest LangChain updates.

Cloudflare AI Search Bundles Full RAG Pipeline in One CLI Command

Cloudflare AI Search wraps crawling, embedding, vector storage, and ranking into one managed service with a single wrangler command and free beta access.

Six Cheaper Rungs Under RAG: When Not to Reach for the LLM

Kezhan Shi maps six deterministic methods that settle most enterprise document requests faster and cheaper than an LLM call—with a full audit trail.

Sentence Transformers v6.0 Adds ColBERT Training in 14.5 Hours on One GPU

Sentence Transformers v6.0 adds MultiVectorEncoder with full ColBERT-style training. A single RTX 3090 run beats every zero-shot retriever by 0.06 NDCG@10.

Row-Level RAG Chunks Cut Table Context by 7.7x

Serialising each table row as its own chunk returns 122 chars instead of 943 on a targeted lookup — a 7.7x context reduction with no parser changes.

AWS Cuts RAG Token Costs 33% With a Two-Call Compression Pattern

A post-retrieval Lambda function routes chunks through Claude Haiku before Claude Sonnet, cutting tokens by 8.6× and costs by 33% across 500K documents.

FBG Multi-Agent Support System Cuts Containment Gap 56%

Fanatics Betting and Gaming's AWS multi-agent system improved containment 56% and resolution 53% in two months, using Claude, Nova Lite, and custom RAG.

Kimi K3's 1M-Token Window Costs 16× More Than RAG on 12 Questions

A controlled 12-question blind trial pits Kimi K3's full 127K-token prompt against a tuned RAG pipeline. Long-context wins on completeness, loses on cost and latency.

LLM Judges Carry Nine Measurable Biases: What to Do

DHS 2026 research catalogues nine exploitable biases in LLM-as-judge pipelines and shows grounded evaluators as the structural fix.

Nine Measurable Biases That Corrupt LLM Judge Verdicts

A DHS 2026 workshop catalogued nine distinct biases in LLM-as-judge pipelines — and showed grounded evaluation as the structural fix.

Harness-1 Shows Smaller Open Models Can Beat Frontier AI at Search

Harness-1 is a 20B open-source search agent that beats GPT-5.4 on recall by moving search memory out of the model and into a structured environment.

Why AI Agents Need a Terminal, Not Just a Vector Database

Researchers propose Direct Corpus Interaction (DCI) — a technique that gives AI agents raw bash-like terminal access to search corpora, outperforming dense retrievers by over 30 points on multi-hop QA while cutting costs by 29%.

Why Vector Databases are Essential for Enterprise AI

A deep dive into embeddings, semantic search, and why standard SQL databases aren't enough for LLM context retrieval.

The Economics of RAG vs. Fine-Tuning in Enterprise AI

A deep dive into architecture strategy: When should your company use Retrieval-Augmented Generation, and when is it worth paying for Fine-Tuning?