Implementing a RAG system: Crawl

I'm starting a "Crawl, walk, run" series of posts on various topics and decided to start with Retrieval-Augmented Generation (RAG). Learn the basics and progress to a production-ready system! Crawl In this phase of your journey, we're going to learn about the core concepts of a Retrieval-Augmented Generation (RAG) system and then apply them in a simple example. We're going to build a Human Resources (HR) agent that can help answer and navigate HR-related questions. Using the Government of British Columbia's HR Policy PDFs as our knowledge base, we will process, chunk, and embed the documents into a local vector database. This allows the agent to provided grounded answers and ensures that every response is rooted directly in the ingested BC government policies. Why RAG? RAG is a very common design pattern that turns a standard LLM into an informed AI agent. Standard models can be a "black box", but RAG gives your agent an "open-book test". It bypasses knowledge cutoffs by linking direct