#rag

Building a Vector Database from Scratch vs Using Pinecone/Weaviate

The question isn't whether you need a vector database. If you're working with embeddings — for RAG, semantic search, recommendations, or anything that converts text to vectors — you need somewhere to store and search them. The question is whether you should build that layer yourself or use something that already exists. Most developers approach this wrong. They either reach for a managed service before understanding what it does, or they spend a week building their own before discovering it breaks at 50k vectors. This article covers both paths honestly, with working code for all three approaches.

RAG (Retrieval-Augmented Generation) Explained with Real-World Examples

LLMs have a memory problem. Ask Claude or GPT-4 about your internal documentation, this quarter's pricing changes, or a contract signed last week — and you'll get one of two outcomes: "I don't know," or something confidently wrong. RAG fixes this. Not by retraining the model. Not by fine-tuning. By handing the model the documents it needs, right before it answers.