Building a RAG Chatbot in 45 Minutes: A No-Code Guide

Learn how to build a RAG chatbot without any programming in just 45 minutes. Perfect for learning or creating solutions for an AI product manager portfolio.

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Building a RAG Chatbot in 45 Minutes: A No-Code Guide

I built a RAG (Retrieval-Augmented Generation) chatbot in just 45 minutes—without writing a single line of code. This is an excellent way to learn or create a solution for an AI product manager portfolio.

RAG Chatbot

But how does it actually work?

First, RAG isn’t just about vector storage. It can retrieve information from various data sources like Google Drive, SQL databases, or plain text files.
I used vector storage in my implementation, so here’s what was required:

Step 1: Generate Embeddings

Data isn’t stored in its raw format.
Instead, it’s split into chunks (e.g., 500–1000 characters per chunk), converted into multi-dimensional vectors, and stored in a vector database.

Step 2: Handle Retrieval, Generation, and User Interface

Users interact through a friendly interface. When a question is asked, it’s converted into a vector and used to retrieve the most relevant chunks.
Finally, a Large Language Model (LLM) uses both the retrieved chunks and the original query to generate an answer.

This is the simplest implementation—Vanilla RAG.

In real-world applications, you might also consider:

  • Adaptive RAG: Dynamically selects or modifies retrieval strategies based on the request (e.g., choosing the best data source or rephrasing the query).
  • Hybrid RAG: Combines multiple retrieval methods, such as keyword + semantic search, or merges results from various sources.

Step 3: Evaluate the RAG System

A RAG system consists of two components that require different evaluation methods: retrieval and generation.
Traditionally, metrics like Recall@k, Precision@k, or MRR are used to evaluate the retrieval component.

A RAG system has three core elements:

  • Question (Q)
  • Retrieved Context (C)
  • Answer (A)

You can build one almost for free using:

  • User Interface: Lovable (free tier is sufficient)
  • Orchestration: n8n (trial / free self-hosted version)
  • LLM: OpenAI’s GPT-4o-mini (less than $2 for 100+ requests)
  • Embedding Model: text-embedding-3-small
  • Vector Database: Pinecone (free tier, Starter edition)
  • Document Source: Google Drive