RAG Implementation

What It Does

A question-answering system that lets users point to any data source - codebases, PDFs, documentation - and get contextual LLM responses.

Stack: LangChain · AWS Aurora (vector store) · Llama 3 (base model)

Why

A foundational experiment that preceded the more complex RAG work at Artha Intelligence. The goal was to understand the full retrieval pipeline from chunking → embedding → retrieval before adding complexity.

You can't debug a broken retrieval system if you don't understand what each stage is doing. Building it from scratch with visible seams - not using a pre-packaged RAG library - was the point.