Most RAG tutorials stop at 'put vectors in a database.' This post covers what actually determines quality: how you chunk documents, which vector search engine to pick, and how to measure and iterate on retrieval performance using Bedrock Knowledge Bases and LLM-as-judge evaluation.
Vector search, semantic search, keyword search, hybrid search â these terms get used interchangeably but they mean different things. This post breaks down what each actually does, when each matters, and why hybrid search wins for RAG.
A beginner-friendly walkthrough of how an LLM actually works end-to-end: from typing a prompt to receiving a response â covering tokenization, embeddings, Transformer layers, KV cache, the training loop, embeddings for search, and why decoder-only models won.
A practical walkthrough of two paths to working with Mistral â the managed API for fast prototyping and self-hosted deployment for full control â with real code covering prompting, model selection, function calling, RAG, and INT8 quantization.
AWS now offers 9 different ways to store and search vectors for RAG workloads. This guide compares every option through the Well-Architected Framework to help you pick the right one.