Building Blocks of RAG: From Understanding to Implementation

How can you stop LLMs from hallucinating? Discover Retrieval-Augmented Generation, the efficient way to ground models in your own data.

#1about 2 minutes

Tech stack for building a RAG application

The core technologies used for the RAG implementation include Python, Groq for LLM inference, LangChain as a framework, FAISS for the vector database, and Streamlit for the UI.

#2about 1 minute

Understanding the fundamentals of large language models

Large language models are deep learning models pre-trained on vast data, using a transformer architecture with an encoder and decoder to understand and generate human-like text.

#3about 3 minutes

The rapid evolution and adoption of LLMs

The journey of LLMs has accelerated from the 2022 ChatGPT launch to widespread experimentation in 2023 and enterprise production adoption in 2024.

#4about 2 minutes

Key challenges of LLMs like hallucination

Standard LLMs face significant challenges including hallucination, unverifiable sources, and knowledge cutoffs that limit their reliability for enterprise use.

#5about 1 minute

How RAG solves LLM limitations

Retrieval-Augmented Generation addresses LLM weaknesses by retrieving relevant, up-to-date information from external data sources to provide accurate and verifiable responses.

#6about 4 minutes

The data ingestion and processing pipeline

The first stage of RAG involves loading documents, splitting them into manageable chunks, converting those chunks into numerical embeddings, and storing them in a vector database.

#7about 2 minutes

The retrieval and generation process

The second stage of RAG handles user queries by retrieving relevant chunks from the vector store, constructing a detailed prompt with that context, and sending it to the LLM for generation.

#8about 4 minutes

Visualizing the end-to-end RAG architecture

A complete RAG system processes a user's query by creating an embedding, finding similar document chunks in the vector DB, and feeding both the query and context to an LLM to generate a grounded response.

#9about 5 minutes

Demo of a RAG-powered document chatbot

A live demonstration shows a Streamlit application that allows users to upload a PDF and ask questions, receiving answers grounded in the document's content.

#10about 2 minutes

Summary and deploying RAG solutions

A recap of the RAG process is provided, along with considerations for deploying these solutions in enterprise environments using managed cloud services or open-source models.