Day 4: RAG: Answer Questions from Your Own Documents | Build an AI Chatbot

Today's Objective

Learn the core concepts of RAG: Answer Questions from Your Own Documents and apply them in practical exercises.

What RAG Is

RAG works in two steps: first, find the relevant parts of your documents that match the user's question (retrieval). Second, include those parts in the prompt and ask Claude to answer based on them (generation). The simplest implementation uses text search rather than vector embeddings — good enough for most use cases.

Terminal — Install Dependencies

npm install natural

rag.js — Document Store and Retrieval

// Simple keyword-based retrieval
class DocumentStore {
  constructor() {
    this.chunks = [];
  }
  
  addDocument(text, metadata = {}) {
    // Split into ~500 character chunks with overlap
    const words = text.split(/\s+/);
    const chunkSize = 80; // words
    const overlap = 20;
    
    for (let i = 0; i < words.length; i += (chunkSize - overlap)) {
      const chunk = words.slice(i, i + chunkSize).join(' ');
      if (chunk.trim()) {
        this.chunks.push({ text: chunk, metadata });
      }
    }
  }
  
  retrieve(query, topK = 3) {
    const queryWords = new Set(
      query.toLowerCase().split(/\W+/).filter(w => w.length > 3)
    );
    
    const scored = this.chunks.map(chunk => {
      const chunkWords = new Set(
        chunk.text.toLowerCase().split(/\W+/).filter(w => w.length > 3)
      );
      let score = 0;
      for (const word of queryWords) {
        if (chunkWords.has(word)) score++;
      }
      return { ...chunk, score };
    });
    
    return scored
      .sort((a, b) => b.score - a.score)
      .slice(0, topK)
      .filter(c => c.score > 0);
  }
}

module.exports = DocumentStore;

server.js — Add RAG to the /chat Route

const DocumentStore = require('./rag');
const docStore = new DocumentStore();

// Load your documents at startup
docStore.addDocument(`
  Your company knowledge base text goes here.
  This can be product documentation, FAQs,
  policies, or any text you want the bot to
  know about. Add as much as you need.
`, { source: 'knowledge-base' });

// Update the /chat route to use RAG
app.post('/chat', async (req, res) => {
  const { message, sessionId } = req.body;
  
  if (!conversations[sessionId]) {
    conversations[sessionId] = [];
  }
  
  // Retrieve relevant chunks
  const chunks = docStore.retrieve(message, 3);
  const context = chunks.length > 0
    ? 'Relevant information:
' + chunks.map(c => c.text).join('

')
    : '';
  
  conversations[sessionId].push({
    role: 'user',
    content: message
  });
  
  const systemPrompt = `You are a helpful assistant.
${context ? context + '

Use the above information to answer if relevant.' : ''}
If you don't know something, say so honestly.`;
  
  const response = await client.messages.create({
    model: 'claude-opus-4-5',
    max_tokens: 1024,
    system: systemPrompt,
    messages: conversations[sessionId]
  });
  
  const reply = response.content[0].text;
  conversations[sessionId].push({ role: 'assistant', content: reply });
  
  res.json({ reply, sourcesFound: chunks.length });
});

Day 5: Deploy

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RAG: Answer Questions from Your Own Documents

Today's Objective

What RAG Is

Supporting References & Reading

Go deeper with these external resources.

Day 4 Checkpoint