Topic Cluster

RAG & Vector Search

Master retrieval-augmented generation, vector databases, embeddings, and semantic search systems.

19 articles in this topic

Mar 3, 2026

Pinecone vs Qdrant: Which Vector Database Wins in 2026?

Qdrant delivers 2x lower latency at half the cost, but Pinecone ships in days with zero ops. We tested both in production—here's which fits your team.

Mar 3, 2026

RAG Reranking: When It Actually Improves Retrieval

Cross-encoder reranking boosted our client's RAG accuracy from 73% to 91%—but added 300ms that killed another's chatbot. Here's how to decide.

Mar 3, 2026

Weaviate Pricing in 2026: Free Tier, Plans, and Real Costs

Weaviate's free sandbox lasts 14 days. We break down Flex ($45/mo), Premium ($400/mo), self-hosted costs, and when each tier actually makes financial sense.

Feb 18, 2026

GraphRAG Implementation: What 12 Million Nodes Taught Us

We built a GraphRAG system with Neo4j for a 14-source enterprise platform. Here's how entity extraction, graph modeling, and query routing work at scale.

Jan 12, 2026

How to Tell If Your RAG System Actually Works

Most teams measure RAG success with vibes, not metrics. Learn the specific evaluation approaches that reveal whether your retrieval pipeline delivers accurate, relevant results.

Dec 22, 2025

How Many Dimensions Should Your Embeddings Have?

384, 768, 1024, or 3072 dimensions? The right choice depends on your data complexity, latency requirements, and storage budget—not the highest number available.

Dec 11, 2025

Pinecone vs Weaviate vs Qdrant: 2026 Vector Database Comparison

Pinecone starts at $70/mo, Weaviate and Qdrant are free to self-host. We tested all three in production—here's where each wins on latency, cost, and scaling.

Dec 8, 2025

How to Chunk Documents for RAG Without Losing Context

Context loss during document chunking kills RAG accuracy. Learn the semantic chunking strategies and overlap techniques that preserve meaning while optimizing retrieval performance.

Nov 28, 2025

Choosing Embedding Models for RAG: What Actually Matters in Production

Real-world framework for choosing embedding models based on production requirements, cost, and performance—not just benchmarks. From 20+ RAG implementations.

RAG & Vector Search

Pinecone vs Qdrant: Which Vector Database Wins in 2026?

RAG Reranking: When It Actually Improves Retrieval

Weaviate Pricing in 2026: Free Tier, Plans, and Real Costs

GraphRAG Implementation: What 12 Million Nodes Taught Us

How to Tell If Your RAG System Actually Works

How Many Dimensions Should Your Embeddings Have?

Pinecone vs Weaviate vs Qdrant: 2026 Vector Database Comparison

How to Chunk Documents for RAG Without Losing Context

Choosing Embedding Models for RAG: What Actually Matters in Production

Explore Other Topics

AI Agents

AI Security

LLMs & Models

AI for Business

AI Development Tools

RAG & Vector Search

Pinecone vs Qdrant: Which Vector Database Wins in 2026?

RAG Reranking: When It Actually Improves Retrieval

Weaviate Pricing in 2026: Free Tier, Plans, and Real Costs

GraphRAG Implementation: What 12 Million Nodes Taught Us

How to Tell If Your RAG System Actually Works

How Many Dimensions Should Your Embeddings Have?

Pinecone vs Weaviate vs Qdrant: 2026 Vector Database Comparison

How to Chunk Documents for RAG Without Losing Context

Choosing Embedding Models for RAG: What Actually Matters in Production

Explore Other Topics

AI Agents

AI Security

LLMs & Models

AI for Business

AI Development Tools