System Designer

🔄 What is RAG?

Retrieval-Augmented Generation (RAG) combines information retrieval with language generation. Instead of relying on training data alone, RAG systems retrieve relevant documents and use them to generate accurate, up-to-date responses.

The RAG Advantage: Turn any LLM into a domain expert by giving it access to your knowledge base. No retraining required, facts stay current, and responses are grounded in your data.

❌ Without RAG

• Knowledge cutoff dates
• Hallucinated facts
• No domain-specific data
• Expensive fine-tuning

✅ With RAG

• Always current information
• Grounded, factual responses
• Your private data included
• No model retraining needed

🎯 Types of RAG Systems

Basic RAG

Simple retrieve-then-generate approach

60-70%

accuracy

Workflow:

User Query→

Vector Search→

Retrieve Docs→

Generate Answer

✅ Pros

• Simple to implement
• Fast responses
• Clear architecture

⚠️ Cons

• Limited reasoning
• Context window limits
• Poor relevance filtering

Best for: Simple Q&A, documentation search, basic chatbots

⚠️ Common RAG Challenges

Retrieval Relevance

Problem: Retrieved documents don't answer the user's question

Common Causes:

• Poor embedding quality
• Inadequate chunking
• Weak query understanding

Solutions:

✓ Use better embedding models (text-embedding-3-large)
✓ Implement hybrid search (vector + keyword)
✓ Add query expansion and rewriting
✓ Use reranking models to filter results

Code Solution:

// Query expansion for better retrieval
async function expandQuery(originalQuery) {
  const expansion = await openai.chat.completions.create({
    model: "gpt-3.5-turbo",
    messages: [{
      role: "user", 
      content: `Generate 3 alternative phrasings of this query: "${originalQuery}"`
    }]
  });
  
  return [originalQuery, ...expansion.choices[0].message.content.split('\n')];
}

🤖 RAG System Demo

Ask a question about AI/ML (try: "How do vector databases work?")

💻 RAG Implementation

// Simple RAG implementation
import OpenAI from 'openai';
import { PineconeClient } from '@pinecone-database/pinecone';

class SimpleRAG {
  constructor() {
    this.openai = new OpenAI();
    this.pinecone = new PineconeClient();
  }

  async query(userQuestion) {
    // 1. Generate embedding for the question
    const embedding = await this.openai.embeddings.create({
      model: "text-embedding-3-large",
      input: userQuestion
    });

    // 2. Search for relevant documents
    const searchResults = await this.pinecone.query({
      vector: embedding.data[0].embedding,
      topK: 5,
      includeMetadata: true
    });

    // 3. Prepare context from retrieved documents
    const context = searchResults.matches
      .map(match => match.metadata.text)
      .join('\n\n');

    // 4. Generate answer using retrieved context
    const response = await this.openai.chat.completions.create({
      model: "gpt-4",
      messages: [{
        role: "system",
        content: "Answer questions using only the provided context."
      }, {
        role: "user", 
        content: `Context: ${context}\n\nQuestion: ${userQuestion}`
      }]
    });

    return response.choices[0].message.content;
  }
}

📊 RAG Evaluation

Retrieval Metrics

• Precision@K: % of retrieved docs that are relevant
• Recall@K: % of relevant docs that were retrieved
• MRR: Mean Reciprocal Rank of first relevant result
• NDCG: Normalized Discounted Cumulative Gain

Generation Metrics

• Faithfulness: Answer grounded in retrieved docs
• Answer Relevance: Response addresses the question
• Context Precision: Retrieved context is useful
• Human Evaluation: Manual quality assessment

Business Metrics

• Response Time: End-to-end latency
• Cost per Query: Embedding + LLM costs
• User Satisfaction: Thumbs up/down ratings
• Task Completion: Did user achieve goal?

Testing Strategy

• Golden Dataset: Curated Q&A pairs
• A/B Testing: Compare RAG variations
• Regression Testing: Prevent quality degradation
• Stress Testing: Performance under load

🚀 Production Best Practices

Architecture

✓ Async processing: Parallel retrieval and generation
✓ Caching strategy: Cache embeddings and frequent queries
✓ Load balancing: Distribute across vector DB replicas
✓ Fallback systems: Graceful degradation when APIs fail

Monitoring

✓ Query patterns: Track common questions
✓ Retrieval quality: Monitor relevance scores
✓ Response quality: Track user feedback
✓ Cost tracking: Monitor API usage and expenses

Security

✓ Access control: User-based document filtering
✓ Data sanitization: Clean inputs and outputs
✓ Audit logging: Track all queries and responses
✓ PII protection: Redact sensitive information

Optimization

✓ Batch operations: Process multiple queries together
✓ Smart chunking: Optimize chunk size and overlap
✓ Reranking models: Improve retrieval precision
✓ Prompt optimization: A/B test prompt variations

🎯 Key Takeaways

✓

RAG = Retrieval + Generation: Combine vector search with LLMs for factual, grounded responses

✓

Start simple, evolve: Begin with basic RAG, add complexity as needed

✓

Quality over quantity: Better retrieval beats more retrieved documents

✓

Measure everything: Track retrieval quality, generation accuracy, and user satisfaction

✓

Handle edge cases: Plan for API failures, poor retrieval, and hallucinations

📝 RAG Systems Knowledge Check

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Building RAG Systems