Build a Full RAG System Before Lunch

Retrieval-Augmented Generation (RAG) is the secret sauce behind modern AI assistants that can answer questions accurately using your specific data. In this tutorial, you'll build a production-ready RAG system from scratch.

What you'll learn

How RAG works and why it matters for enterprise AI
Optimal knowledge base structure for retrieval accuracy
Best practices for citation quality
API integration patterns (Node.js & Python)
Production deployment best practices

1. What is RAG and Why It Matters

Retrieval-Augmented Generation (RAG) is an AI architecture that combines the power of large language models with your organization's specific knowledge. Instead of relying solely on the LLM's training data, RAG systems:

Retrieve relevant documents from your knowledge base
Augment the AI's context with this retrieved information
Generate responses grounded in your actual data

The Problem RAG Solves

Traditional LLMs have a critical limitation: they can only respond based on their training data, which may be outdated or lack your specific domain knowledge. This leads to:

Without RAG

Hallucinated or outdated information
No citations or verifiable sources
Generic responses lacking specificity
Can't answer about proprietary data

With RAG

Accurate, grounded responses
Full citation support
Domain-specific expertise
Always up-to-date with your data

2. Architecture Overview

Chat.co implements RAG using a robust, scalable architecture. Here's how the components work together:

User Query

Embedding

Vector Search

LLM + Context

Response

Component Breakdown

1. Document Processing Pipeline

When you upload documents, they're chunked into semantic segments, embedded using state-of-the-art models, and stored in a vector database for fast retrieval.

2. Vector Search Engine

User queries are embedded and compared against your document embeddings using cosine similarity to find the most relevant chunks.

3. Context Assembly

Retrieved chunks are assembled into a coherent context, ranked by relevance, and passed to the LLM along with the user's question.

4. Response Generation

The LLM generates a response grounded in the provided context, with citations pointing back to the original source documents.

3. Setting Up Your Knowledge Base

The quality of your RAG system depends heavily on how you structure and prepare your knowledge base. Here's how to set it up for optimal results.

Document Preparation Best Practices

Key Principle: The AI can only be as good as the data you provide. Clean, well-structured documents lead to accurate, helpful responses.

Structure Your Content

Use clear headings — H1, H2, H3 structure helps the system understand content hierarchy
Keep paragraphs focused — One topic per paragraph improves retrieval accuracy
Include metadata — Titles, dates, and categories help with context
Avoid scanned PDFs — Use text-based documents or OCR-processed files

Organizing by Category

Group related documents together for better retrieval:

knowledge-base/
├── product/
│   ├── features.pdf
│   ├── pricing.pdf
│   └── comparisons.pdf
├── support/
│   ├── faq.pdf
│   ├── troubleshooting.pdf
│   └── getting-started.pdf
└── policies/
    ├── terms-of-service.pdf
    ├── privacy-policy.pdf
    └── refund-policy.pdf

Upload via Dashboard

Navigate to your chatbot's Sources page
Click Add Source → Upload Files
Drag and drop your documents (max 50MB per file)
Wait for processing to complete (green checkmark)
Verify source count in the dashboard

Upload via API

For automated workflows, use our API to upload documents programmatically:

// Node.js example
const FormData = require('form-data');
const fs = require('fs');
const axios = require('axios');

const form = new FormData();
form.append('file', fs.createReadStream('document.pdf'));

const response = await axios.post(
  'https://api.chat.co/v1/chatbots/{chatbotId}/sources',
  form,
  {
    headers: {
      'Authorization': 'Bearer YOUR_API_KEY',
      ...form.getHeaders()
    }
  }
);

console.log('Document uploaded:', response.data);

4. Optimizing Citation Quality

Citations are what make RAG systems trustworthy. They allow users to verify information and build confidence in your AI assistant.

Citation Best Practices

Use descriptive document titles
Name files clearly: "2024-Product-Pricing-Guide.pdf" is better than "doc1.pdf"
Include page numbers
Chat.co automatically tracks page numbers for PDF citations
Structure content with headers
Clear section headers improve citation specificity
Avoid duplicate content
Multiple documents with the same content can confuse citation attribution

Pro Tip: Enable showCitations in your chatbot appearance settings to display citations in the chat interface.

5. API Integration Examples

Integrate your RAG system into custom applications using our API. Here are examples in popular languages.

Node.js / TypeScript

const axios = require('axios');

const API_KEY = 'sk_live_your_api_key';
const BASE_URL = 'https://api.chat.co/client/v1';

const client = axios.create({
  baseURL: BASE_URL,
  headers: {
    'Authorization': `Bearer ${API_KEY}`,
    'Content-Type': 'application/json'
  }
});

async function askQuestion(question) {
  // 1. Create conversation
  const { data: conv } = await client.post('/conversations');
  const conversationId = conv.data.conversation.id;

  // 2. Send message and get response
  const { data: response } = await client.post(
    `/conversations/${conversationId}/messages`,
    { message: question }
  );

  // 3. Extract answer and citations
  const { content, citations } = response.data.botResponse;

  return {
    answer: content,
    citations: citations.map(c => ({
      title: c.title,
      url: c.url,
      snippet: c.snippet
    }))
  };
}

// Usage
const result = await askQuestion('What is your return policy?');
console.log('Answer:', result.answer);
console.log('Sources:', result.citations);

Python

import requests
from dataclasses import dataclass
from typing import List, Optional

API_KEY = 'sk_live_your_api_key'
BASE_URL = 'https://api.chat.co/client/v1'

@dataclass
class Citation:
    title: str
    url: str
    snippet: str

@dataclass
class RAGResponse:
    answer: str
    citations: List[Citation]

def ask_question(question: str) -> RAGResponse:
    headers = {
        'Authorization': f'Bearer {API_KEY}',
        'Content-Type': 'application/json'
    }

    # Create conversation
    conv_response = requests.post(
        f'{BASE_URL}/conversations',
        headers=headers,
        json={}
    )
    conversation_id = conv_response.json()['data']['conversation']['id']

    # Send message
    msg_response = requests.post(
        f'{BASE_URL}/conversations/{conversation_id}/messages',
        headers=headers,
        json={'message': question}
    )

    data = msg_response.json()['data']['botResponse']

    return RAGResponse(
        answer=data['content'],
        citations=[
            Citation(
                title=c.get('title', ''),
                url=c.get('url', ''),
                snippet=c.get('snippet', '')
            )
            for c in data.get('citations', [])
        ]
    )

# Usage
result = ask_question('What is your return policy?')
print(f'Answer: {result.answer}')
for citation in result.citations:
    print(f'Source: {citation.title}')

Streaming Responses

For a better user experience, stream responses in real-time. See the API Documentation for streaming examples.

6. Testing & Validation

Before deploying to production, thoroughly test your RAG system to ensure accuracy and reliability.

Testing Checklist

Known-Answer Testing

Ask questions where you know the correct answer. Verify the response is accurate and properly cited.

Edge Case Testing

Test questions outside your knowledge base. The bot should gracefully indicate when it doesn't have information.

Ambiguous Query Testing

Test vague questions to see how the system handles disambiguation.

Citation Verification

Verify that citations point to the correct source documents and page numbers.

7. Production Deployment Checklist

Before going live, ensure your RAG system is production-ready with this checklist.

All documents uploaded and processed successfullyKnown-answer tests passing with accurate citationsEdge cases handled gracefullyAPI integration tested in staging environmentError handling implemented for API failuresRate limiting configured appropriatelyMonitoring and logging set up

Congratulations!

You've built a production-ready RAG system. Your AI assistant can now provide accurate, cited answers based on your organization's knowledge.

Key Takeaways