Chatbot with RAG Technology | 매거진에 참여하세요

Introduction: Chatbots Need to Be Smarter Than Ever

Chatbots these days are truly impressive — they can tell you the weather, write code, and even draft reports.

But how do AI chatbots get so smart?

While powerful language models like GPT are amazing, they alone can’t handle real-time data, specific domain knowledge, or personalized conversations well.

That’s where RAG (Retrieval-Augmented Generation) comes in.

In this post, I’ll walk you through how to build your own custom chatbot using RAG, from start to finish.

Why Choose a RAG Chatbot?

Models like GPT-4 are powerful but have two key limitations:

• No memory: They can forget earlier parts of a conversation as it grows longer.

• No real-time updates: They don’t know about events or data created after their training cutoff (e.g., 2024 company earnings).

RAG fixes these issues.

What is RAG?

RAG isn’t just a simple chatbot — it combines:

2. - Input question

3. - Vector search (Retrieval)
   → Finds relevant documents from a vector database

4. - Generation
   → Uses the retrieved documents plus the question to generate an answer via a language model

Thanks to this architecture, RAG chatbots can:

• - Answer questions based on internal company documents

• - Chat using your own Notion, PDFs, or Wiki data

• - Customize conversations for specific user scenarios

RAG Chatbot Architecture

Core flow:
User question → [Embedding] → [Vector DB search] → [LLM answer generation] → User reply

Tech stack examples:

Document Collection & Preprocessing

The key to a good RAG chatbot is what data it’s based on.

For example, a chatbot answering internal policy questions needs:

• - Policy manuals (PDF)

• - Company Notion docs

• - Meeting notes

• - Excel/CSV files

Preprocessing tips:

• - Chunking: GPT can’t read very long texts at once. Split documents into 500-1000 character chunks with some overlap to keep context.

• - Embedding: Convert each chunk into a vector (e.g., 1536-dimensional).

• - Store in Vector DB: Keep embeddings ready for fast similarity search when queries come in.

How Question Processing Works

1. User question → Create embedding vector

const embedding = await openai.createEmbedding({ input: "What's the company leave policy?", model: "text-embedding-ada-002" });

2. Search top-K similar docs in vector DB (e.g., top 3-5)

const results = vectorDB.similaritySearch(embedding, { k: 3 });

3. Generate response by passing docs + question to GPT-4

const prompt = Using the following info, answer the question: ${results.join("\n")} 
Question: ${userQuestion}; const response = await openai.createChatCompletion({ model: "gpt-4", messages: [{ role: "user", content: prompt }] });

Example: HR Chatbot Based on Company Docs

User: “How many weeks of maternity leave are available?”

Chatbot:
“According to the company’s maternity leave policy, you can take up to 90 days (about 12 weeks) around the childbirth date.

For twins, it’s extended to 120 days.”

Because it references real documents, the answers are accurate and trustworthy.

How to Improve and Extend Your Chatbot

• - Useful features:

• Save chat history for user context

• Create FAQ templates for common questions

• Separate modes for different domains (e.g., “Dev mode,” “HR mode”)

• - Advanced ideas:

• Multimodal RAG: Combine text + images (e.g., manuals with pictures)

• Integrate PDFs + API data (ERP/CRM) for richer answers

Conclusion: Build a Truly Smart AI Chatbot Beyond GPT

RAG isn’t just about feeding GPT knowledge — it’s a powerful way to build your own chatbot tailored to your company.

It overcomes GPT’s limitations around context, accuracy, and up-to-date info.

AI is becoming your team’s real partner — start building your RAG chatbot today!