Preparing data for RAG requires specialized ETL pipelines. After building pipelines for 50+ RAG systems, I’ve learned what works. Here’s the complete guide to ETL for vector embeddings.
Tag: Semantic Search
Fine-Tuning vs RAG: A Comprehensive Decision Framework
Last year, I faced a critical decision: fine-tune our LLM or implement RAG? We chose fine-tuning. It was expensive, time-consuming, and didn’t solve our core problem. After building 20+ LLM applications, I’ve learned when to use each approach. Here’s the comprehensive decision framework that will save you months of work. Figure 1: Fine-Tuning vs RAG… Continue reading
Advanced RAG Patterns: Beyond Basic Retrieval
Six months ago, I thought RAG was simple: retrieve chunks, send to LLM, done. Then I built a system that needed to answer questions about 50,000 technical documents. Basic retrieval failed spectacularly. That’s when I discovered advanced RAG patterns—techniques that transform RAG from a prototype into a production system. ” alt=”Advanced RAG Patterns” style=”max-width: 100%;… Continue reading
Production RAG Architecture: Building Scalable Vector Search Systems
Three months into production, our RAG system started failing at 2AM. Not gracefully—complete outages. The problem wasn’t the models or the embeddings. It was the architecture. After rebuilding it twice, here’s what I learned about building RAG systems that actually work in production. Figure 1: Production RAG Architecture Overview The Night Everything Broke It was… Continue reading
Vector Database Comparison: Pinecone vs Weaviate vs Qdrant vs Chroma – Choosing the Right One for Your RAG Application
Last March, a 3AM alert changed everything. Our Pinecone bill had tripled overnight, and I spent the next three months migrating between vector databases, learning hard lessons about what actually matters. Let me share what I discovered—and what I wish someone had told me. Figure 1: Comprehensive comparison of vector database options The Night Everything… Continue reading