# Building Scalable AI Systems with RAG
Retrieval-Augmented Generation (RAG) has revolutionized how we build AI systems that can access and utilize external knowledge. In this post, I'll share my experience building production RAG systems.
## What is RAG?
RAG combines the power of large language models with external knowledge retrieval. Instead of relying solely on the model's training data, RAG systems can:
- **Retrieve** relevant information from external sources
- **Augment** the prompt with this information
- **Generate** responses based on both the model's knowledge and retrieved data
## Architecture Overview
Here's a typical RAG architecture I've implemented:
```typescript
interface RAGSystem {
vectorStore: VectorDatabase;
embeddings: EmbeddingModel;
llm: LanguageModel;
retriever: DocumentRetriever;
}
class RAGPipeline {
async query(question: string): Promise<string> {
// 1. Convert question to embeddings
const queryEmbedding = await this.embeddings.embed(question);
// 2. Retrieve relevant documents
const documents = await this.vectorStore.similaritySearch(
queryEmbedding,
{ limit: 5 }
);
// 3. Augment prompt with context
const context = documents.map(doc => doc.content).join('\n');
const prompt = `Context: ${context}\n\nQuestion: ${question}`;
// 4. Generate response
return await this.llm.generate(prompt);
}
}
```
## Key Challenges
### 1. Vector Database Selection
Choosing the right vector database is crucial:
- **Pinecone**: Great for production, managed service
- **Weaviate**: Open-source, good for complex schemas
- **Supabase Vector**: Perfect for PostgreSQL users
### 2. Chunking Strategy
How you split documents affects retrieval quality:
```javascript
function smartChunk(text, chunkSize = 1000) {
// Split by paragraphs first
const paragraphs = text.split('\n\n');
const chunks = [];
let currentChunk = "";
for (const paragraph of paragraphs) {
if ((currentChunk + paragraph).length > chunkSize) {
if (currentChunk) {
chunks.push(currentChunk.trim());
}
currentChunk = paragraph;
} else {
currentChunk += "\n\n" + paragraph;
}
}
if (currentChunk) {
chunks.push(currentChunk.trim());
}
return chunks;
}
```
## Production Considerations
### Caching Strategy
Implement multi-level caching:
1. **Query-level cache**: Cache exact question matches
2. **Embedding cache**: Cache embeddings for common queries
3. **Document cache**: Cache frequently accessed documents
### Monitoring & Evaluation
Track these metrics:
- **Retrieval accuracy**: Are we finding relevant documents?
- **Response quality**: Human evaluation scores
- **Latency**: End-to-end response time
- **Cost**: Token usage and API costs
## Real-World Implementation
At **Nordra Inc**, I built a RAG system for our HRIS platform:
- **Knowledge Base**: 10,000+ HR documents
- **Vector Store**: Supabase with pgvector
- **Embeddings**: OpenAI text-embedding-3-large
- **LLM**: GPT-4 for generation
Results:
- 85% accuracy on HR queries
- 2.3s average response time
- 40% reduction in support tickets
## Best Practices
1. **Hybrid Search**: Combine vector similarity with keyword search
2. **Reranking**: Use a reranker model to improve retrieval quality
3. **Prompt Engineering**: Craft prompts that guide the model effectively
4. **Fallback Strategies**: Handle cases when retrieval fails
## Conclusion
RAG systems are powerful but require careful engineering. Focus on:
- Quality data preparation
- Robust retrieval mechanisms
- Comprehensive evaluation
- Production monitoring
The future of AI applications lies in systems that can dynamically access and reason over external knowledge.
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*Have questions about RAG implementation? Feel free to reach out!*