Building and Understanding Basic Retrieval-Augmented Generation (RAG) System

#rag #ai #python #gemini

Generative AI models are powerful, but they have one major limitation—they only know what they were trained on. They cannot reliably answer questions about your company's latest documents, internal policies, or newly published research without additional context. This is where Retrieval-Augmented Generation (RAG) comes in.

I have created a basic lab and will add on future labs with more advanced understanding of RAG

Lab 1 introduces the fundamental concepts of RAG by building a simple document-based question-answering application using Python and an affordable Gemini model. Instead of relying solely on the model's internal knowledge, the application retrieves relevant information from a document and provides that context to the LLM before generating an answer.

In this lab, you will load(loaders/document_loader.py) multiple .txt files (data folder), extract its contents, split and clean (utils/text_utils.py) the texts. When a user asks a question, the application converts the question to an array of words with clean approach removing stop words such as "a", "an", "the", "and", "or", "is", "in", "on", "of", "to" these words are then compared against the retrieved documents (retreiver/keyword_retriever.py) and the most words matched document is selected. This document becomes the retrieved context and passed on to LLM(llm/prompt_builder.py) to produce an accurate, grounded response.

The objective of Lab 1 is to understand the complete end-to-end retrieval pipeline. By implementing each component yourself, you will gain practical insight into how document retrieval, prompt construction, and LLM generation work together.

By the end of this lab, you will be able to: