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RAG

How to Build a RAG-Powered Chatbot (The Simple Stack)

You don’t need a PhD in AI to get started. Here’s a simple architecture you can build with modern tools

In short, RAG makes LLMs more flexible, accurate, and reliable for real-world tasks.
RAG Workflow

Choose a Language Model

Use open-source models like LLaMA, Mistral, or Falcon, or hosted ones like Cohere, Anthropic Claude, OpenAI GPT or Azure OpenAI.

Set Up a Vector Store

Store your document chunks in a fast, searchable database like:

    • FAISS
    • Pinecone
    • Weaviate
    • ChromaDB

Use a Text Embedding Model

To convert your documents into searchable vectors using Hugging Face, OpenAI, or Cohere embeddings.

Build the Retriever

When a user asks something, use vector similarity search to grab the top relevant document chunks.

Pass the Context to the Generator

Combine the user’s query and the retrieved docs, then feed them to the language model to generate the final answer.

Wrap It in a Chat Interface

Build a front-end using React, Next.js, or your preferred framework. Connect it with your backend via APIs.

Optional: Add Memory, Feedback, and Analytics

For a more human-like experience, add chat history, user feedback, and usage analytics.